Biomimetic hairy affective-touch sensory AI interface

Domain: chemistry
Published: 2026-03-18
Editorial outcome: major_revision

Abstract
The absence of tactile emotion perception limits artificial intelligence (AI) in decoding social behaviors encoded in human physical contact. Here, a biomimetic hairy sensing interface is developed to capture affective touch’s spatiotemporal characteristics and intrinsic features, allowing for accurate emotion recognition. The hairy interface directly induces neuromimetic electric pulse signals under external stimuli without the need for a spike coding circuit. Through a bistage hairy structure, homogeneous nanomesh manufacturing process and isoline theory, it achieves high force detection sensitivity (0.67 N-1) and spatial precision (1.61 mm localization accuracy across 100 cm2). The interface replicates biological C-LTMRs’ behavior, establishing the first bioelectronic analog of affective touch transduction. Integration with hybrid neural network setting (convolutional neural network and contextual large language model) enables real-time emotion recognition with 82.37% accuracy across individualized touch patterns. This neuromorphic tactile framework facilitates the closed-loop human-AI emotional interaction, advancing toward humanoid robots capable of natural affective communication.

Introduction
Recent advancements in artificial intelligence (AI) have revolutionized how humans and robots interact1. However, achieving natural social engagement with humans remains a challenge, primarily due to the lack of affective tactile perception2. Bridging this gap requires soft tactile mapping sensors and electronic skins (e-skins)3 that mimic the human somatosensory system to enable bidirectional communication between human and robot. In humans, tactile perception, integrates emotional and cognitive processing via specialized C-low threshold mechanoreceptors (C-LTMRs), going beyond mere physical sensation. These receptors, first identified in mammalian skin4, are intrinsically linked to affiliative behaviors and social bonding5-7. They enable the recognition of affective touch—a critical modality for decoding emotional states during physical contact. For AI-driven systems to interpret human emotions authentically, it is essential to integrate soft tactile sensors and e-skins capable of replicating this multidimensional affective sensing. Affective touch8, mediated by spatiotemporal stimuli such as contact force, velocity, and location9, plays a pivotal role in reducing anxiety, fostering trust, and enhancing mental well-being10. However, quantifying these dynamic cues remains a significant challenge. Conventional rigid sensors struggle to capture the nuanced interplay of tactile parameters (e.g., dynamic force gradients, microvibrations) while ensuring user comfort11,12. Prior attempts, such as pressure-sensing arrays on rigid robotic interfaces11, often prioritize gesture recognition over emotional interpretation and their mechanical inflexibility can compromise the naturalness of interaction13. In contrast, soft tactile mapping sensors14,15 and e-skins16,17 provide deformable, biocompatible interfaces that mimic human skin’s compliance, enabling seamless and emotionally resonant interactions. These systems excel in real-time, high-resolution acquisition of multi-dimensional tactile data18, including spatial (e.g., force distribution, contact area) and temporal (e.g., stroking speed, duration) features—essential for correlating tactile patterns with emotional states. While soft devices have advanced as deformable interfaces for multi-dimensional data acquisition (Supplementary Table 1), a critical gap remains in decoding how these tactile signatures correlate with emotional states.

Intriguingly, nature offers a blueprint for such affective sensing through hairy skin biomechanics. In mammals, hair follicles integrate specialized low-threshold mechanoreceptors (C-/Aδ-/Aβ-LTMRs) that form lanceolate endings around hair shafts19,20 (Fig. 1a). This architecture transforms hair structures into ultrasensitive force transducers: hair deflections under gentle touch (< 1 mN) induce mechanoelectric conversion at the follicle-neural interface, enabling the detection of micron-level displacements, vibration frequencies, and stroke direction in real time21. Crucially, this system is tuned to affective cues. For instance, light caresses (0.1-10 cm/s) preferentially activate C-LTMRs’ pathways, which are linked to emotional valence processing in the insular cortex22,23, while filtering out non-affective mechanical noise. Inspired by this biological paradigm, recent advances in hair-inspired tactile sensors demonstrate unprecedented sensitivity to affective touch signatures24,25. Li et al. proposed an all-optical, flexible whispering-gallery-mode resonator25 that exhibits good directionality, high displacement sensitivity and the ability to detect tiny force. Another study presented a magnetosensitive hair based micro-origami sensor array for tactile perception, achieving real-time multidirectional tactile applications such as direction-resolved touch sensation, pressure sensing, air and fluid flow detections24. However, a majority of current research still focuses on isolated mechanical sensing functions rather than on the integration of spatiotemporal tactile patterns necessary for inferring emotion.

In this article, a biomimetic hairy affective-touch sensory AI interface (BioAI2) is developed to bridge the gap between the physical perception of affective touch and the accurate recognition of emotions. As illustrated in Fig. 1b and Supplementary Figure 1, by employing an Ecoflex hairy force-sensitive layer with strong electron affinity in conjunction with the triboelectric mechanism, the BioAI2 can directly generate neuromimetic spike signals through triboelectric coupling effects, where precisely controlled friction-induced charge transfer at molecular interfaces creates quantized electron pulses. This self-powered signal generation mechanism, fundamentally driven by nanoscopic triboelectrification phenomena involving sub-10 nm interfacial charge displacements, operates without the assistance of external circuits. Decoding these spike-like output signals allows the device to perceive multi-dimensional information, including static and dynamic forces, position, and stroking speed. To enhance its force-sensing capability, a bistage hierarchical height hairy structure was designed, resulting in a force-sensing accuracy of 0.67 N-1, which exceeds that of comparable single height structures by more than threefold. Additionally, by pioneering the use of polyvinyl chloride and dibutyl adipate (PVC-DBA) in electrostatic spinning, an ultra-homogeneous conductive nanomesh layer was fabricated to produce smooth and natural isolines, a significant improvement over volatilized films, achieving a high positioning accuracy of 1.61 mm across a large sensing area of 100 cm2. By introducing an isoline theory that explicitly counters the edge distortions common in traditional positioning methods, the system achieves high-precision, distortion-free continuous motion tracking. Interestingly, our experimental findings reveal an inverted U-shaped relationship between the device’s output pulse frequency and stroking velocity, closely mirroring the behavior of human C-LTMRs. Given the established direct link between C-LTMRs and emotional states26, this relationship allows emotional states to be assessed based on the device’s spike frequency. This marks the pioneering effort to emulate the functionality of human C-LTMRs and introduces a quantifiable standard for evaluating emotional states, a milestone that has not been reached in previous studies.

Owing to its lightness (0.25 g/cm2) and flexibility (Supplementary Figure 1), the BioAI2 can be assembled on robots effortlessly, enabling real-time fusion of multi-dimensional tactile data (force, velocity, contact topology). By leveraging convolutional neural networks (CNNs) and large language models (LLMs), we implement a real-time tactile based emotional perception system with emotional context decoding ability for robots, allowing AI to interpret affective tactile cues. This represents a significant advance in the field of embodied intelligence27, particularly in exploring embodied emotions. We believe that making AI understand the affective touch, in other words, affective sensory AI, can facilitate the establishment of a more harmonious and natural human-machine relationship, which is particularly crucial for applications of AI in education28, healthcare29, psychological counseling30, and other fields.

Results
Structure and affective touch sensing mechanism of the BioAI2
Analogous to human hair and skin, the BioAI2 comprises a hairy force-sensitive layer, mechanoreceptors, and neuro-signal transmission fibers (Fig. 1b). The hairy force-sensitive layer is fabricated from Ecoflex, a material with strong electron affinity that enables the BioAI2 to generate stronger triboelectric signals in response to external stimuli. The mechanoreceptors, which function as triboelectric mechanoreceptors, consist of the hairy layer and a PVC nanomesh homogeneous conductive layer. It converts external stimuli into spike-like signals. The PVC nanomesh conductive layer, fabricated by electrostatic spinning to ensure high uniformity, is crucial for achieving high precision position sensing. Finally, the neural signal transmission fibers consist of four electrodes positioned at the four corners of the conductive nanomesh layer, along with conducting wires that transmit multi-dimensional information to the core processing module through a minimal number of data channels.

Figure 1c illustrates the affective touch sensing process of the BioAI2 which is designed to mimic the functionality of human hairy skin. (i) Upon tactile stimulation, the hairs of the BioAI2 deflect in a manner analogous to human hairs. This cyclical contact and separation between the hairs and fingers triggers a triboelectric effect, generating neuromimetic spike signals within the BioAI2 that are functionally similar to the neural responses in biological tactile afferents. These neuromimetic signals are inherently encoded with multidimensional information such as stroking velocity, pressure, and location. (ii) The generated signals are then transmitted to the core processing module via four neuro-signals transmission fibers. (iii) Subsequently, in humans, the somatosensory cortex analyzes the multimodal sensory information from LTMR’s signals, whereas the BioAI2 employs decoupling algorithms to extract and decouple the multidimensional information embedded within the impulse streams. (iv) Finally, at the level of emotional perception, the human brain processes affective touch stimuli, performs context analysis, and elicits emotional responses via a thalamo-cortico-amygdala circuit31. In the BioAI2, this functional hierarchy is implemented computationally. A CNN processes the tactile stimuli, while a LLM is responsible for in-depth contextual analysis and generating appropriate emotional feedback.

Neuromimetic stroke sensing of the BioAI2 enabled by triboelectricity
Affective touch is often mediated by gentle stroking. Studies have shown that C-LTMRs responded differential to the spatiotemporal characteristics of strokes, such as velocity and force32. Therefore, accurately sensing these stroking parameters is essential for capturing affective qualities of touch. As depicted in Fig. 2a, when a hand strokes the BioAI2, the fingers repeatly contact and separate from the hairs, generating continuous neuromimetic signals via the triboelectric mechanism. To further elucidate the underlying mechanism, a mechanical simulation is conducted using COMSOL Multiphysics, with the stress distribution plots depicted in Fig. 2b. Additionally, since the triboelectric output is significantly influenced by the magnitude of the contact area, the variation curve of this area is also measured during the simulation to provide a deeper insight into the signal generation process (Fig. 2d(i)).

During the contact-separation process, a charge transfer occurs based on the electron cloud model33. Because human skin has a weaker electron affinity than the hairy structure which is made of soft silicone (Ecoflex 00-30), positive charges accumulate on the finger’s surface, while negative charges form on the hair. This induces corresponding countercharges in the underlying conductive layer to maintain overall charge balance, as illustrated in Fig. 2c. The experimentally measured transferred charges and voltage output are presented in Fig. 2d(ii-iii). To delineate the signal dynamics, the entire process is divided into five stages (S1-S5) based on the change in contact area (Fig. 2d(i)), which is a primary factor influencing the magnitude of the triboelectric transferred charge. Initially, as the finger approaches the first hair (S1), the countercharges slowly decline in the conductive layer due to the electrostatic effect. Upon full contact (S2), a further diminishment of countercharges in the conductive layer occurs, because the positive charges on the finger progressively neutralize the negative charges. As the finger continues to move, it will gradually disengage from the hair, until detaching ultimately (S3), during when the countercharges decrease slowly. Then, since there is no frictional hindrance of the hair anymore, the finger rapidly approach the next hair until it completely contacts with the next hair (S4), which produces a sharper downtrend in countercharges due to a larger contact area. Finally, during slow separation from the second hair (S5), the countercharges return to the baseline. The variation of the countercharge during this process can be mapped as the transferred charges of the conductive layer as experimentally verified and visually presented in Fig. 2d(ii). According to Ohm’s law, the voltage fluctuations can be mathematically derived as follows:

V = IR = -R × (dQ/dt) (1)

Thereby, a sharp positive voltage peak will be generated in stage S4, which is shown in Fig. 2d(iii). Furthermore, a theoretical model of BioAI2 is established and the triboelectric pulse generation mechanism are elaborated in Supplementary Note 1 and Supplementary Figure 2-4.

The outputs of the BioAI2 in response to different stroking speeds are presented in Fig. 2e. To examine the relationship between the output spike frequency and stroking speed, an experiment was conducted using hairs with standardized dimensions: long and short hairs had heights of 3 mm and 1.5 mm, respectively, and a constant width (d) of 2 mm. Figure 2f presents a detailed comparison of hairs featuring distinct gap sizes (w). Notably, the results exhibit an inverted U-shaped trend in signal frequency, suggesting that beyond a critical velocity threshold, the spike frequency decreases. The reason behind the downtrend of the frequency is probably caused by the overlap of high-frequency signal peaks resulting from multiple fingers at excessive sliding speed. Meanwhile, higher stroking speed can lead to inadequate contact separation, generating spikes with amplitudes that are too low to be detected effectively.

It is also notable that the result is consistent with the nerve signals output of the C-LTMRs which also demonstrated an inverted U-shaped result under different brushing velocities32 (Fig. 2g). In biological systems, C-LTMRs precisely employ such nonlinear encoding to distinguish between affective caresses and non-emotional mechanical stimuli. The reproduction of this characteristic neuromorphic coding pattern enables the device to mimic the neural basis of emotional valence processing in the insular cortex22,23. This consistency is not superficial but is grounded in a multi-level biomimetic analogy. First, from a structural perspective, both the BioAI2 interface and biological C-LTMRs operate within the context of hairy skin, where artificial hairs or natural hair follicles serve as intrinsic mechanical levers that amplify gentle tactile stimuli. Second, the encoding principle is neuromimetic: we have developed a coupled biophysical model where the triboelectric effect (governing charge generation in response to mechanical contact-separation) drives a leaky integrate-and-fire (LIF) neuron model (Supplementary Note 2, Supplementary Figure 5). This simulates the mechano-transduction and encoding process in primary afferents, from mechanical stimulus to receptor potential and ultimately to action potential generation, thus providing a principled foundation for the spike generation mechanism that goes beyond a mere visual analogy.

Through reasonable adjustment of the hairy structure, the signal output can well approximate the behavior of biological neural signals. It is found that the output frequency of the interface presents the highest frequency between 10 mm/s and 200 mm/s when the gap reaches 3 mm. This consistency with C-LTMRs provides a quantifiable standard for evaluating emotional states by observing the characteristics of the signal (pulse frequency) which has not been achieved before. In addition, the outputs of different d with the gap of 3 mm are also explored in Fig. 2h and the result with the hair width of 2 mm presents the best fit. Thus, the device structure in subsequent experiments adopts this configuration. This quantitative replication confirms that BioAI2 effectively captures the key nonlinear encoding strategy used by C-LTMRs to distinguish affective caresses from non-emotional mechanical stimuli.

Owing to its ability to generate spikes without an analog-to-digital converter (ADC) and its negligible static power consumption, this neuromimetic output has the potential to integrate seamlessly with the human body for a natural human-machine interface34,35. Furthermore, by adhering to biological coding principles, the artificial sensory signals ensure compatibility with future neural interfaces. This paves the way for bidirectional communication with the peripheral nervous system, enabling genuine embodied affective interaction.

Multi-dimensional sensing capability of continuous force and position
Continuous force sensing. Ignoring the multi-dimensional nature of touch limits our comprehensive understanding of the complexities and depth of tactile interaction36. To overcome the limitation of conventional single-parameter sensing, our device integrates capabilities for measuring both force intensity and spatial location. The force sensing ability is achieved by synergistically combining the electrostatic induction effect with a two-stage height hairy structure. To enable the measurement of both static and dynamic forces using a four-electrode design (Fig. 3a), the transferred charge (Q) is used as the key observation variable. The equivalent circuit of the device is shown in Fig. 3b, from which Q can be calculated as:

Q = ∫I∙dt = ∫(I1+I2+I3+I4)∙dt = ∫ (V1+V2+V3+V4)/Rex ∙dt (2)

Therefore, the sum of the voltage integration of four electrodes can be leveraged to monitor the charge variation continuously. The waveforms of voltages and calculated charge are displayed in Fig. 3c, clearly showing the static force measurement results under different applied forces. To augment force sensitivity, we design a bistage hierarchical hairy structure, drawing inspiration from the varying heights of sweat hairs that critically contribute to the amplification of afferent stimuli force perception. As illustrated in Fig. 3d, this hierarchical hair architecture significantly improves force sensitivity, achieving a value of 0.67 N-1. This represent an enhancement of over three times compared to monostage configurations of equivalent height. Mechanistically, this performance improvement stems from the structure’s graduated interfacial contact modulation under low-pressure. The dual-height configuration enables the sequential activation of microhair subsets: taller filaments (Stage I) initiate initial charge transfer at minimal loads (<1 N), while shorter ones (Stage II) sustain the contact area expansion as force increases. This hierarchical engagement reduces the threshold contact charge (Q0) by 69% (from 1.24 nC to 0.38 nC), and amplifies the relative charge variation rate (ΔQ/Q0/ΔF) by 372% through optimized triboelectric coupling. Furthermore, the dynamic force response under different frequencies is also tested and shown in Supplementary Figure 6. Furthermore, the four-electrode design raises the question of potential positional effects on the charge output. Here, the charge outputs are measured in three different positions (corner, edge and center) as shown in Supplementary Figure 7. The result reveals that the charge output is position-independent.

Distortion-free position sensing. Spatial positioning capability is also a key characteristic of the affective touch. To achieve high positioning ability, four-electrode-based design is adopted here. As shown in Fig. 3a, due to the differences in the resistances between the touch points and four electrodes, the output voltages of different electrodes would vary with location. For example, when touching different points (P1, P2, P3, P4, P5) (Fig. 3e), the corresponding voltage outputs are illustrated in Fig. 3f.

Several position sensing strategies based on 4 electrodes including resistive37,38 and capacitive46 are widely reported, in addition to sensor arrays such as acoustic tomography40,41, capacitive42 and magnetic47, which require massive sensor and acquiring channels. However, one bottleneck for the practical applications of these methods is their low positioning accuracy due to the severe distortion deriving from approximate coordinate calculation formulas37:

x = (V1+V2)/(V1+V2+V3+V4) (3)

y = (V1+V4)/(V1+V2+V3+V4) (4)

Herein, we have established an electrical theoretical model with the aim of providing a more refined method for position calculation. As stated in Supplementary Note 3, the stated formulas can be calculated as:

x ≈ (1/R1 + 1/R2) / (1/R1 + 1/R2 + 1/R3 + 1/R4) (5)

y ≈ (1/R1 + 1/R4) / (1/R1 + 1/R2 + 1/R3 + 1/R4) (6)

Thus, we need to theoretically calculate the resistances between the touch point and four electrodes (R1, R2, R3, R4). The rectangular conductive film fabricated via electrospinning can be viewed, from a microscopic perspective, as a uniform conductive plane composed of interconnected nanorods. In our case, the film’s dimensions (10 cm × 10 cm) are significantly larger than the inter-electrode spacing, allowing us to approximate number of these connections as infinity. Therefore, it can be approximated as a rectangular grid formed by connecting an infinite number of identical resistors r. Although this grid has physical boundaries, the number of resistors within it approaches infinity, as illustrated in the Supplementary Figure 8. Consequently, the problem is transformed into calculating the resistance between any arbitrary point (m, n) on the plane and the electrode located at (0,0), where the number of resistors along both the horizontal and vertical dimensions of the grid is effectively infinite.

Consider a rectangular network of resistors connected in an array of M × N nodes forming a network with free boundaries as shown in Supplementary Figure 9. Number the nodes by coordinates (m, n), 0 ≤ m ≤ M − 1, 0 ≤ n ≤ N − 1 and denote the resistances along the two principal directions by r and s.

According the two-point resistance from Wu48, the resistance Rfree between two nodes r1 = (x1, y1) and r2 = (x2, y2) is:

Rfree(r1, r2) = ∑∑ [|ψ(m,n);(x1,y1) - ψ(m,n);(x2,y2)|]^2 / λ(m,n) (7)

where ψ is eigenvector, λ is eigenvalue, θ = mπ/M, φ = nπ/N.

As illustrated in Supplementary Figure 8, the model is configured with the following parameters: r = s, r1 = (0,0) and r2 = (x, y). The resistance between the two nodes can thus be expressed as:

Rfree(r1, r2) = (rx/M) + (sy/N) + (2r/MN) * ∑∑ [cos(x+1/2)θ cos(y+1/2)φ - cos(1/2)θ cos(1/2)φ]^2 / (2-cosθ-cosφ) (8)

Given that the conductive plane is sufficiently large, M and N can be regarded as approaching infinity. Under this condition, Equation (8) can be further simplified to:

lim Rfree(r1, r2) = (x/M) + (y/N) + (1/π^2) ∫∫ [cos(x+1/2)θ cos(y+1/2)φ - cos(1/2)θ cos(1/2)φ]^2 / (sin^2θ + sin^2φ) dθdφ (9)

It can be observed from the derivation that Equation (9) is applicable to conductive films with finite boundaries. This confirms that the model aligns well with the physical reality, and consequently, the localization method derived from this theoretical framework does not introduce significant edge effects.

Therefore, the relationship between electrode 1 (R1) and the position of the touch point (x,y) can be plotted as shown in Supplementary Figure 10. Similarly, the resistance between touch point and other electrodes can be also acquired. And their corresponding isolines can be drawn as shown in Supplementary Figure 11 and Fig. 3g (Details are elaborated in Supplementary Note 4), from which we can find that the x, y and corresponding ratio obey a nearly linear relationship in the central region while the linear relationship fails in the edge region, defined as the near-linear and non-linear regions, respectively. This is the critical factor causing the distortion of the formulas. One way to solve the distortion problem is to improve the calculation formulas. Here, three different calculation formulas are calculated and their isolines are drawn in Supplementary Figure 12-13. The results reveal that the formulas calculated by the reciprocal of voltage demonstrate the largest linear area and best anti-distortion effect (Supplementary Figure 13-14). Nevertheless, these formulas still cannot avoid the problem of distortion. Hence, inspired by the isolines theory40, we used the isolines in Fig. 3g to quantify the relationship between the position and the ratio of voltages. Figure 3(h) visually explains our method for determining the accurate touch coordinate by leveraging the isolines: (1) When a touch occurs at a specific point on the interface, it generates four voltage pulses with peaks V1, V2, V3, and V4; (2) From these voltages, the instantaneous ratios x0 and y0 can be calculated using the Equation (5) and (6); (3) Then the specific isolines corresponding to the values x=x0 and y=y0 are identified from the precomputed set of isolines covering the entire plane; (4) These two isolines are plotted together (as shown in Fig. 3(h)). The point where the x isoline and the y isoline intersect is the calculated touch point. This method accounts for the non-linear distribution of the ratios, providing anti-distortion capability.

To validate its effectiveness, the initial requirement is the preparation of a conductive plane with the utmost uniformity. As illustrated in Supplementary Figure 15a(i), conventional solution evaporation methods can lead to variations in surface tension due to factors such as environmental changes and uneven evaporation rates of the solution, resulting in the formation of a conductive film with inconsistent thickness and, consequently, uneven conductivity distribution. This phenomenon is particularly noticeable when manufacturing large areas. This significantly impacts the final positioning accuracy. The isoline obtained through the solution evaporation method, as shown in Supplementary Figure 15a(ii), reveals significant contour fluctuations in localized areas, which are attributed to the uneven thickness of the conductive film. To address this issue, the electrospinning method is adopted (Supplementary Figure 15b(i)), where a conductive solution based on a PVC and DBA plasticizer mixture is sprayed onto a flexible polyethylene glycol terephthalate (PET) substrate and subsequently cured to form a uniform flexible spun film. The precise control over the spraying speed and position afforded by electrospinning enables the preparation of a uniform thin film. To assess homogeneity of the fabricated film at the micro-scale, we performed SEM on our conductive film. Two random samples (0.5 cm × 0.5 cm each) were taken from a large-area film (10 cm × 10 cm) prepared by electrospinning. As shown in Fig. 3i and Supplementary Figure 16a-e, the SEM results clearly demonstrate that the electrospinning process successfully produced a dense and uniformly distributed nanomesh structure. Furthermore, we measured the thickness profile of the nanomesh through AFM (Supplementary Figure 16f-g). The data showed a maximum height variation of less than 1.5 µm, which is negligible for a film with a total thickness exceeding 100 µm, and an average roughness (Ra) of 150 nm. These results provide strong evidence of high structural uniformity at the microscopic level. Furthermore, the isoline of the nanomesh membrane, as depicted in Supplementary Figure 15b(ii), demonstrates much more continuous and stable characteristics compared to that obtained via the solution method (Supplementary Figure 15a(ii)), attesting to its exceptional uniformity. The result demonstrates that nanotextured interface engineering (nanomesh structure) can reconcile conflicting requirements of high uniformity and large-area fabrication. Meanwhile, the measured isoline of the electrostatic spinning PVC nanomesh layer is similar to the contour distribution in Fig. 3g and further verifies the theory. To investigate the performance of the algorithm, the spatial distribution of position error from two methods is exhibited in Fig. 3k. The inference accuracy of the position of the traditional ratio-based method (average error of 3.49 mm, max error of 11.64 mm) dramatically declines with farther from the center while that of isoline theory (average error of 1.61mm, max error of 3.60 mm) is relatively constant. Meanwhile, the repeatability of the theory is also demonstrated in Supplementary Figure 17. For further verification, the locating results for different patterns based on traditional ratio methods and the isoline theory are compared in Fig. 3l. The ratio-based formulas present obvious distortions due to the influence from the non-linear region while the isoline theory ensures good pattern reconstruction.

Multi-finger positioning. Another drawback of traditional four-electrode methods is the single touch point restriction, due to the crosstalk of different touching signals making it impossible to distinguish different touch points. For the four-electrode based positioning devices powered with continuous AC power supplies37,38, the signals of different fingers will couple together which directly causes errors in position sensing. For the four-electrode based device with the traditional plane interface as shown in Supplementary Figure 15c(i), the touch activated triboelectric signal lasts nearly 50 ms, and the corresponding time-frequency diagram indicates that the signal only exhibits low-frequency components. Thus, multiple touches would easily cause overlaps of different signals.

To address this dilemma, a higher temporal resolution of the signal is desired to avoid overlapping. Here, the designed biomimetic hair-like structure endows the output signal with a prominent high-frequency characteristic. As shown in Supplementary Figure 15c(ii), The exceptionally brief duration (<2 ms) of these spike-like pulses, contrasting with the prolonged signals from traditional plane interfaces, provides intrinsic temporal separation for decoupling simultaneous or near-simultaneous touches. Its time-frequency diagram presents significant high-frequency components compared to that of the plane interface. Attributing to these components, the multiple touches can be fully reconstructed by comparing the ratios of peaks using the isoline theory. As depicted in Fig. 3m, the super-high temporal resolution of the structure enables effective distinction between signals (peak ① and peak ②) generated in a very short time. Attributing to the high temporal resolution, continuous and even the two-finger sliding trajectory are effectively reconstructed (Fig. 3n, Supplementary Figure 18-19, further details are discussed in Supplementary Note 5). According to the result, it can be found that the located points during the sliding process appear in turns which indicates that two fingers slide simultaneously and the signals induced by sliding do not interfere with each other. Furthermore, other complicated patterns can also be reconstructed as illustrated in Fig. 3o and Supplementary Figure 20. In Fig. 3p and Supplementary Figure 15d, a comparison between the proposed method with state-of-the-art works is presented. It can be seen that this is the first work achieving a good trade-off between position error and electrode number. Meanwhile, it also presents outstanding functionality, and multipoint positioning ability while keeping low-powered characteristics which have not been reported before. The performance of the BioAI2 is also compared to state-of-the-art soft tactile sensors and comprehensively demonstrates the advantages of the proposed method in tactile sensing (Supplementary Table 1).

The multi-dimensional tactile perceptual characteristic of the BioAI2 perfectly contributes to its subtle spatiotemporal feature capturing ability. The BioAI2 is then utilized as a touchpad to manipulate a robot hand and its arm, as illustrated in Fig. 4a-b. The precise position sensing ability of the BioAI2 endows multiple different control commands by a single touchpad (Supplementary Figure 21). These commands were respectively executed by touching or sliding different positions of the BioAI2. The force sensing ability enables the quick conversion between coarse tuning (strong force) and fine tuning (small force), e.g., touch with a large force brings the fastest speed, touch with a small force brings a middle speed, sliding on the surface brings the slowest speed (Fig. 4c). These settings enable multistage control and allow more precise position control. Attributing to the bimodal sensing ability, the robot hand can perform fine operations such as conducting a chemical experiment (Fig. 4b-d, Supplementary Figure 22 and Supplementary Movie 1).

Furthermore, for affective touch, accurate distinguishment between different gestures is also crucial. To realize diversified gesture recognition (Fig. 4e), a 1D CNN-long short-term memory (1D CNN-LSTM) hybrid network is corpora te with the BioAI2, utilizing the voltage waveform and charge waveform inputs to capture the spatiotemporal characteristics (stroking speed, location) and force information, respectively (Fig. 4f(i)). Benefiting from the feature extraction ability of CNN and the high sensitivity to time of the LSTM, the accuracy and loss of the network quickly converge to 99.6% and 0.015 (Fig. 4f(ii)). As evidenced in the confusion matrix (Fig. 4f(iii)), the gesture recognition system achieves an overall accuracy of 99.5%, indicating the outstanding spatiotemporal characteristic capturing capability of the BioAI2 and the high efficiency and robustness of the deep learning algorithm. Furthermore, a robot dog is applied to execute predefined actions as the feedback (Fig. 4g), demonstrating the high application potential of the BioAI2 in human-machine interaction (Supplementary Movie 2 showing the real-time interaction process).

Affective touch sensation and analysis based on the BioAI2
Experiment setting for the acquisition of affective touch data. Because of its excellent multi-dimensional tactile perception capability, the BioAI2 effectively captures the spatiotemporal characteristics of touch, enabling a deeper understanding of its affective meaning. It is important to note that the same gesture can convey vastly different emotions depending on the context. Therefore, interpreting affective touch should rely not on gesture type alone, but on detailed action characteristics. To explore this, we conduct an experiment to investigate the features of eight representative gestures (Fig. 4e) under different emotional states. A critical challenge in such studies is constructing an accurate mapping between these low-level action features and discrete emotion labels. The pursuit of this mapping is grounded in growing evidence linking tactile kinematics to emotional experience (Details are shown in Supplementary Note 6). From a neurophysiological perspective, specific tactile action features, such as velocity and force, have been shown to modulate emotional responses, mediated by specialized afferent pathways23. Furthermore, psychophysical studies showed that humans can accurately decode distinct emotions, including anger, fear, love, and gratitude, via touch alone, confirming that tactile actions carry emotionally relevant information56,57. This provides a solid theoretical basis for establishing a quantifiable relationship between how a touch is performed and the emotion it conveys.

To minimize subjectivity inherent in actor-simulation methods13 where participants’ imagination and acting skills can compromise ecological validity, we employed more reliable emotion elicitation stimuli. Previous emotion research reveals that emotional film clips with both scene and audio are shown reliable and effective for evoking subjects’ emotions in the laboratory58,59. Accordingly, as outlined in Fig. 5a, volunteers were asked to watch specific videos with both scene and audio (details of the videos are shown in Supplementary Figure 24a) to evoke different emotions (Fig. 5a(i)) and implement different gestures on the BioAI2 (Fig. 5a(ii)) when the video ended. The raw touch data were recorded through the BioAI2 and further processed to extract the spatiotemporal characteristic through analysis in the time domain and frequency domain (Fig. 5a(iii), more details in Supplementary Figure 23 and Methods). Meanwhile, as shown in Fig. 5a(iv), they were requested to rate their affective states and classify their emotions using the self-assessment manikin (SAM) with valence and arousal dimensions, which is one of the most popular self-reporting tools in the literature60. The detail of the protocol used in the experiment is illustrated in Supplementary Figure 24b. Then, an artificial neural network is applied to analyze the features rooted in the affective touch data (Fig. 5a(v)) and executed the classification of the emotions and gestures (Fig. 5a(vi)).

In this experiment, to effectively elicit different emotions from volunteers, 9 video clips (Supplementary Figure 24a) were selected for 3 emotions (video no. 1-3 for positive, 4-6 for neutral, 7-9 for negative). Supplementary Figure 24c illustrates the rating distributions of the different film clips from 13 participants on the arousal-valence plane. According to Russell model61, the distribution areas of three different emotions (positive, neutral, negative) are drawn as rectangular areas with different colors of red, blue and green, respectively. Some of the voltage and charge outputs during the experiment are shown in Supplementary Figure 25. It can be observed that the rating scores of different video clips are mainly concentrated on corresponding emotion areas. Notably, a few parts of the rating scores are not within the range of the corresponding emotions, which is probably because there exist variations in the effectiveness of videos in eliciting emotional responses from different individuals. These data were recognized as noise and discarded in the following experiments. In addition, the mean ratings for arousal and valence of different video clips are illustrated in Supplementary Figure 24d-e. The results were in good agreement with the theory. The valence scores gradually decreased in the order of positive-neutral-negative, and the arousal score of neutral was the lowest. These data clearly demonstrate that these videos played an effective role in inducing specific emotions in agreement with expectations.

Analysis of the collected data. After the experiment, the tactile data under different gestures and emotions is recorded. To further illustrate the discrepancy of the data, the characteristic parameters such as spike frequency, force, mean of voltage signals, time of duration and standard deviation of the voltage amplitude (std) from different gestures are compared in Fig. 5b. In the diagram, the actions such as push, stroke, and tickle, elicit higher frequency pulse signals. Specifically, the high frequency outputs of push and stroke are attributed to the sliding of surfaces, whereas that of tickle primarily stems from the rapid nature of the action itself, further underscored by its short duration. Furthermore, actions that incorporate a forceful component, such as press, push, and slap, exhibit significant force output, which showcases the force sensing ability of the BioAI2. Notably, slap displays a lower average value of voltage output but possesses a higher standard deviation, distinguishing it markedly from other gestures. From these parameters, it can be seen that the BioAI2 effectively captures the characteristics inherent in different gestures.

To statistically validate the separability and significance of these observed signal features, we conduct two types of hypothesis tests, 1) Kruskal-Wallis test and 2) multivariate analysis of variance (MANOVA) with action type and emotion type as dependent variables. The Kruskal-Wallis test results demonstrate highly significant effects for both action type and emotion type across key parameters. For pulse frequency, the test statistics were H = 10885.923 (p < 0.001) against action and H = 1219.658 (p < 0.001) against emotion. Similarly, force (H = 18172.467, p < 0.001), time (H = 10800.832, p < 0.001), and standard deviation (H = 18148.679, p < 0.001) showed significant separability, while mean voltage did not (H = 220.571, p = 0.641). This confirms that features like pulse frequency are systematically modulated by actions and emotions, providing a statistical foundation for their role in tactile encoding. The MANOVA results reinforced the Kruskal-Wallis findings: pulse frequency exhibited a highly significant effect on both action (F = 4964.098, p < 0.01) and emotion (F = 25.427, p < 0.01). Force, time, and standard deviation also showed significant effects, whereas mean voltage had no significant impact on emotion classification (F = 2.239, p = 0.107).

In order to further analyze the relationship between emotion and action, spike frequency and force with significant characteristics are selected for comparison. Figure 4c displays the comparison of the distribution of spike frequency and force under different emotions from different gestures. As evident in the spike frequency part, for actions involving a sliding process, e.g., push and stroke, positive emotional states elicit a higher spike frequency output, whereas negative emotional states exhibit an inverse trend. This observation is consistent with the human physiological output as shown in Fig. 2g. However, this does not apply to tickle, which does not involve sliding action. In addition, the lower part of the figure reveals that a negative mood presents a higher force for all gestures, which means negative emotions tend to involve a high force which also corresponds with previous studies on humans56. These two different characteristics present a strong relationship to emotion and thus they can be considered as important factors for emotion recognition.

Deep learning for emotion recognition. In addition to the investigation of statistical analysis of the data itself, a neural network is trained to extract affective touch characteristics and perform emotion recognition due to the complexity of the mass data. To effectively extract the valid features from the sequence of data, a continuous wavelet transform (CWT) is applied to transform the data into an image (224×224×3), thereby enhancing the multi-scale and time-frequency localization of signal features and facilitating the analysis of the signal’s spatiotemporal characteristics. Furthermore, as stated before, the spike frequency and force show a strong correlation with emotional state, and both are also appended to the image inputs. As shown in Fig. 5a(iii) and Supplementary Figure 23, the magnitudes of the spike frequency are calculated every second and are rendered as different colors at the top part of the picture. The force is also rendered as different colors below the frequency parts. Some samples of the processed images under varying gesture types and emotion states are shown in Supplementary Figure 26. Then the pictures are inputted into a neural network for training (Fig. 5a(v)). Since the gesture characteristics of different people vary greatly, the network is trained through leave-one-subject-out cross-validation (LOSOCV) to ensure its validity. The tactile data of a single volunteer is isolated as a test set to avoid overfitting the network to the collected data.

To determine the optimal network architecture for emotion recognition, we conducted comparative analyses of several deep learning models. Given our strategy of converting sequential signals into an image classification task, a ResNet-based CNN architecture was selected for its demonstrated excellence in extracting hierarchical spatial features from image-like data. As shown in Supplementary Figure 27a and Supplementary Table 4, the accuracy of the neural network with different structures is compared. Comparative experiments with 1D CNN-LSTM, temporal convolutional networks (TCNs), which process data as 1D sequences, and transformer architectures, revealed that the ResNet-based CNN achieved superior performance on our dataset, as supported by the results summarized. Furthermore, extensive ablation studies were performed to dissect the contribution of different input representations and features (Fig. 5d, Supplementary Figure 27b and Supplementary Table 4). These studies evaluated models trained on CWT images, and specifically isolated the impact of frequency information and force information embedded within the CWT spectrograms. The findings unequivocally demonstrated that while frequency information was crucial for both gesture and emotion recognition tasks, the inclusion of force information provided a substantial positive boost to emotion recognition accuracy, underscoring that the physical intensity of an interaction carries critical affective cues. Consequently, the model leveraging combined frequency and intensity information within the ResNet-based CNN framework achieved the highest overall emotion recognition performance, fully justifying our architectural and feature selection.

The confusion matrices for gesture and emotion recognition using the network are shown in Supplementary Figure 27c-d and Supplementary Figure 28. The average gesture recognition accuracy of the CNN achieves 98.92%, showing excellent gesture recognition capability. Meanwhile, despite being affected by the diversity of volunteer actions from the test set, the average emotion recognition accuracy reaches 82.37%, which also presents superior performance compared to previous studies (Fig. 5e). The features learned by the neural network are projected into 2D space using the t-SNE method, and the feature is visualized in Fig. 5f. On the basis of the result, the network can well distinguish the features between different gestures, and yet the ability to distinguish different emotions varies greatly depending on the gestures, e.g., knock and stroke. Specifically, the accuracy of different emotions among different gestures are displayed in Fig. 5g, from which the “stroke” presents the highest recognition accuracy among the gestures which is probably attributed to the rich distinguishing features within it (force and frequency). It reveals that the “stroke” possesses a better ability to convey emotional information which is in agreement with the previous study62. Meanwhile, according to Fig. 5f, there are no significant distinguishing features between negative and positive emotions of “knock” and “pat”. This indicates that these gestures show a low correlation to valence of the emotion which is the key difference between negative and positive. Meanwhile, “slap” and “push” present strong differences in force parameters and highest accuracy when the emotion is negative, showing a strong relationship with negative emotion. Other gestures exhibit medium distinguishing characteristics among all three classes of emotions. According to the above analysis, “stroke” is preferable for expressing emotion, and thus it is chosen for the following emotional interaction experiment. Meanwhile, the characteristics of the BioAI2 are compared to state-of-the-art affective touch sensors, demonstrating the advantages of the affective touch sensing ability of this work (Supplementary Table 5).

To further interpret the decision-making process of the CNN model, we performed Gradient-weighted class activation mapping (Grad-CAM) analysis. The Grad-CAM results consistently revealed that high-frequency components within the CWT spectrograms were the most discriminative features across all eight gestures and three emotion categories. As illustrated in the activation heatmaps (see Supplementary Figure 29), the model intensely attended to regions corresponding to transient, rapid changes in the triboelectric signal, suggesting that high-frequency dynamics are critical cues for both gesture identification and emotion inference. This observation aligns with the neurophysiological principle that affective touch perception is often encoded in rapid tactile dynamics. In gestures where force serves as a key differentiating factor, such as press, stroke, and push, the Grad-CAM heatmaps also showed significant activation in regions encoding force information. This underscores the model’s ability to incorporate force-related cues when distinguishing between emotionally or physically distinct tactile interactions. Moreover, for gestures characterized by sustained spectral patterns, such as stroke and tickle, the network allocated additional attention to specific frequency bands, indicating its sensitivity to spectral shapes in emotional tactile expression. These visual explanations corroborate the quantitative performance observed in Fig. 5g, where gestures exhibiting richer feature variations (e.g., stroke) achieved higher emotion recognition accuracy.

Close loop of natural emotional interaction integrated with Chat Generative Pre-Trained Transformer (ChatGPT)
While AI with affective touch sensing ability can recognize emotion, this represents only the first step toward achieving natural emotional interaction between humans and robots/machines. The comprehensive emotional interaction process encompasses not only emotion recognition but also the comprehension and expression of emotions. Figure 6a conceptually illustrates the closed-loop process of natural emotional interaction, exemplified by the human-dog interaction. When a human gently strokes a dog, the dog can broadly discern the owner’s emotion through affective touch. However, to attain a deeper understanding of human emotions, the dog must incorporate additional contextual information, a vital element in the further interpretation of affective cues63,64. These contexts, including the specific scenario, the characteristics of both the human and the dog, and their relationship, provide essential clues for the comprehension of emotion. Drawing upon this emotional understanding, the dog then expresses its emotions through its behaviors toward the human. The human, in turn, receives emotional feedback from the dog. This cycle, which starts from a human action and ends with the human receiving emotional feedback, constitutes a closed loop of natural emotional interaction.

The complexity of the emotional understanding required in this interaction cannot be handled by a simple set of rules, necessitating the use of advanced large language models that have demonstrated their efficacy in the realm of affective computing65. Specifically, the ChatGPT is integrated into this emotional interaction framework primarily due to its advanced reasoning capability, which enables context-aware understanding and appropriate response generation. It excels by dynamically incorporating contextual cues into its inference process, adapting responses based on the interaction setting, user identity, and emotional history to ensure relevance and interpersonal appropriateness. Moreover, its ability to comprehend through dialogue and generate stable, emotionally congruent text makes it highly suitable for real-time emotional interaction loops, supporting consistent affective expression. Compared to other Transformer architectures, such as BERT, which excels in comprehension tasks like sentiment classification when fine-tuned on specific datasets, GPT provides distinct advantages in zero-shot and few-shot scenarios where extensive task-specific training is not feasible66,67 (Supplementary Note 7). This robust few-shot and zero-shot learning capability, stemming from its generative pre-training and auto-regressive design, makes GPT particularly suitable for the dynamic and evolving context of natural emotional interaction proposed in our study. The entire process has been depicted in a flowchart as shown in Fig. 6b.

The BioAI2 performs emotion recognition by extracting features from affective touch. To evaluate its performance, a volunteer is requested to watch videos designed to elicit neutral, positive, or negative emotions while simultaneously stroking the device. The BioAI2 records the resulting neuromimetic voltage output (Fig. 6c(i)), and the corresponding spike frequency (Fig. 6c(ii)) is analyzed. The results reveal a clear correlation between the spike frequency and the video category. Assuming the videos effectively induce the intended emotional state, the frequency of the output shows a positive correlation with emotional satisfaction as stated in Fig. 2g. This indicates that the spike frequency can serve as a key metric for quantifying emotional state, a approach distinct from previous works. The neural network described in the last section is then applied to classify the emotion based on these characteristics. The result, shown in Fig. 6c (iii), demonstrate that the network successfully extracts the spatiotemporal features and recognizes emotions.

The ChatGPT is integrated to process gestures and emotional information from the BioAI2, interpret the emotion, and enable expressive behavior through a physical entity. A robot dog named Bittle, equipped with the BioAI2 on its back, is used for its flexible motion control. To facilitate this, the ChatGPT is preloaded with Bittle’s available action commands. By providing scenario context to the ChatGPT, the probability of it selecting different dog actions in response to various human emotions is mapped (Fig. 6d). Clear differences in action choices across emotions indicate that these actions can effectively represent different emotional states, thereby enhancing the robot dog‘s contextual emotional feedback.

The application of the BioAI2 in human-robot emotional interaction is demonstrated in Fig. 6e. The demonstrations are set up with two representative scenarios: going to work and coming home. The closed-loop interaction process is described for the “going to work” scenario, as an example. The contextual information includes scenario (go to work), the owner’s characteristics (patient and compassionate) and dog’s characteristics (outgoing and cheerful), and their relationship (close). When the owner strokes the robot dog, the BioAI2 senses and captures the spatiotemporal features of the touch. The CNN then extracts affective touch features, yielding preliminary emotional information (e.g., positive) and classifying the type of gesture (e.g., stroke). The ChatGPT receives this contextual information and the affective touch information from the neural network. Leveraging its emotional computing ability, the ChatGPT refines the provided three different classes of emotion into more specific sub-emotions (see Supplementary Figure 30). When prompted with “What is the owner’s mood?”, the ChatGPT provides a nuanced judgment of the owner’s emotion, explaining its reasoning. Upon follow-up questioning (“How do you respond?”), the ChatGPT can properly plan its behaviors and execute the action sequence (e.g., sniff, jump, nod) to express the emotion. From these actions, the owner can obtain positive emotional feedback from the robot dog and have a pleasant state of mind, which provides a better feeling of companionship to them. The complete workflow is illustrated in Supplementary Figure 31, and a video demo is provided in Supplementary Movie 3.

The integration of ChatGPT is pivotal for achieving natural emotional interaction, primarily due to its superior contextual understanding and flexible decision-making capabilities, which distinguish it from traditional rule-based or simple classifier-based systems. ChatGPT demonstrates a remarkable ability to grasp nuance and interpret emotional intent based on the specific scenario. For example, we implemented another experiment that applied the same interaction gesture (tickle) on the BioAI2 without considering the emotional information extracted from the gesture was also performed (as shown in Supplementary Figure 32 and Supplementary Video 4). The ChatGPT presents completely different emotional understanding and executes diverse motion feedback under different scenarios, characteristics of the dog and their relationship. The result indicates the significant impact of contextual information on ChatGPT’s decision during emotional interaction. Meanwhile, through comparative experiment validation in Supplementary Note 8, both contextual information and tactile emotion cues are crucial for ChatGPT to yield a more confident, precise, and contextually appropriate emotional interpretation than either modality alone.

Unlike systems that rely on a one-to-one mapping of input to a pre-defined output, resulting in predictable and rigid interactions, ChatGPT’s language understanding enables it to generate intelligent, autonomous decisions on how to respond emotionally. This allows for unpredictable, lifelike, and more engaging communication, moving beyond stale, programmatic interactions. Specifically, the robot dog can utilize any combination of its 10 distinct actions (Fig. 6d) for emotional feedback. Even when limiting the number of actions per response to three, the system can produce up to 10 (for one action) + 10^2 (for two actions) + 10^3 (for three actions) = 1110 distinct combinations, ensuring significant variability and unpredictability. As summarized in Supplementary Table 6, which compares several e-skin-based emotional HRI works, our ChatGPT-based robot dog demonstrates the highest level of intelligence by effectively contextualizing information and diversifying response types.

Discussion
Critical to naturalistic interaction between humans and AI is their ability to perceive affective states in the other. Our work focuses on developing a soft, wearable and biomimetic hairy affective touch sensory AI interface that enables the perception of affective touch through which electronic skins can decode emotional cues via physical contact. The BioAI2 leverages a triboelectrification-driven, self-powered mechanism that generates neuromimetic signals through nanoscale interfacial charge displacement at molecular contacts, directly producing quantized electron pulses without external circuits. Through the rational design of a bistage hierarchical hairy structure, the BioAI2 achieves a force detection sensitivity of 0.67 N-1. Furthermore, a nanomesh structure is incorporated into the device, optimizing its spatial position precision and enabling a high localization accuracy of 1.61 mm across a 100 cm2 area. This demonstrates that nanotextured interface engineering can reconcile conflicting requirements of high uniformity and large-area fabrication.

Distinguished from previous research on affective touch, the BioAI2, with its hairy structure, represents the first attempt to provide C-LTMRs like neuromimetic electric outputs, allowing for the direct quantification of emotional characteristics. Moreover, the neuromimetic output encodes multi-dimensional information (e.g., force, position, and stroking speed) into 1D signals, substantially reduces the transfer of redundant data between the sensing units and the computing terminal and enhances the sensing unit’s scalability. By leveraging neural network to extract multi-dimensional spatiotemporal characteristics rooted in affection touch, the BioAI2 achieves high recognition accuracy for gesture (98.92%) and emotion (82.37%), even under significant individual variations. This result validates that the biomimetic spike coding inherently contains sufficient entropy for machine-interpretable emotion recognition. When integrated with LLMs, the BioAI2-based system utilizes affective touch to establish a seamless emotional interaction loop between humans and robot dogs. This not only enhances the naturalness of human-AI interactions, but also underscores the significant potential of affective touch in future human-robot collaboration scenarios.

Currently, the BioAI2 still has rooms for improvement, such as limited coverage area, a relatively small gesture dataset and a lack of other sensing modalities (e.g., temperature) that are also important for a comprehensive perception of affective touch. Future works could focus on refining the fabrication process, expanding the dataset for various gestures, and integrating additional sensing mechanisms. It is also crucial to establish a large-scale, comprehensive affective touch dataset, which would significantly improve the accuracy of complex, multi-dimensional affective touch recognition and optimize AI’s feedback capabilities across diverse scenarios. Nevertheless, as a biomimetic device, the BioAI2 represents a crucial advancement and demonstrates superior advantages in simultaneous multi-dimensional sensing and emotion perception. Through its ability to decode the emotional signals embedded in touch, the BioAI2 shows considerable potential across key domains. It can provide scientifically grounded insights for infant care, serve as a supportive tool in mental health interventions, and facilitate more natural and empathetic human-robot interactions, thereby bridging advanced technology with essential human requirements for well-being.

Methods
Fabrication of BioAI2. A schematic illustration of the fabrication process is shown in Supplementary Figure 33. First, the A and B components of Ecoflex were poured into a beaker and well stirred. Then, the mixture was dropped on a 3D-printed mold and the mold was placed into a vacuum oven to get rid of the air in the mixture. The mixture was then dried at room temperature and formed a hairy layer. The conductive nanomesh layer was fabricated by electrostatic spinning as shown in Supplementary Figure 15b. Specifically, the used solution was prepared by mixing commercial PVC powder (Scientific Polymer Products Inc., Mw 275 000, CAS: 9003-22-9), with DBA (Macklin, CAS: 105-99-7) as a plasticizer, and DMF (Macklin, CAS: 624-49-7) as the solvent. The weight fraction of PVC and DBA is 6 wt% and 12 wt%, respectively. The voltage of the electrostatic spinning was set as 20 kV and -3 kV. The conductive nanomesh layer was sprayed onto a transparent PET substrate fixed on the roller in the electrospinning machine. Four Ag electrodes were then prepared on the four corners of the conductive layer using conductive silver paste. Finally, the hairy layer, the conductive nanomesh layer and a PET substrate were assembled to complete the fabrication of the BioAI2.

Characterizations. The voltage data was measured by an oscilloscope (Siglent SDS 6034 H10 Pro) with a 10 MΩ impedance. The force applied to the sensor was quantified using a force gauge (Pubtester, TST-01H). A linear motor (Linmot, E1200) was applied to perform dynamic response measurement of the BioAI2. For the stroking speed sensing experiment, the interface was fixed on the linear motor, and the stroking speed was controlled by the linear motor. A SEM (ZEISS GeminiSEM 300) and an AFM (Bruker Dimension Icon) were applied here to observe the microstructure of the sensor.

1D CNN-LSTM hybrid network for gesture recognition. As shown in Fig. 3k, a 1D CNN-LSTM hybrid network was developed and assessed on MATLAB R2023b. To achieve simultaneous analysis of spatiotemporal characteristics and force information, the network was inputted with voltage signals (length set as 1000) and charge signals (length set as 100). They were then transmitted into feature extracting parts, which consisted of two modules: a 1D convolution module (1D convolution layer, ReLU layer and Maxpooling layer) and a LSTM module (flatten layer, LSTM layer, and flatten layer). For the voltage waveform, more 1D convolution layers were applied due to the complexity of voltage signals. The outputs of the feature extracting part were then combined through the addition layer and finally calculated through the full connected layer and softmax layer. The dataset was split with around 80% for training and 20% for testing. The adaptive moment estimation (Adam) algorithm was used to optimize. The initial learning rate was set to 0.001.

Emotion-evoking experiments. In total, 13 volunteers of different gender groups participated in the emotion-evoking experiments. The volunteers are between the ages of 22 and 30 (11 males and 2 females, no statistical method was used to predetermine sample size). The sex of participants was based on self-report. We did not perform any sex-based analyses. We did not assess their gender, as we did not anticipate the effect of gender on our study outcomes. All the experimental procedures involving human subjects were conducted in accordance with the guidelines for study procedures by Southeast University. All participants were informed with written consent.

Before experiments, volunteers were required to thoroughly clean their hands, relax, and read informed consent. The experiment was conducted in a closed room with a laptop computer and the BioAI2. The BioAI2 was connected to a data acquisition board (AD 7606) which can collect multi-channel data continuously in real-time. All the volunteers watched a total of 9 video clips, experienced 3 different emotions and performed 8 different gestures on the BioAI2. The details are shown in Supplementary Figure 24a-b. After the experiment, the volunteers were required to clean their hands thoroughly. The data were analyzed using the MATLAB App and converted into waveforms or CWT images. The dataset finally consists of 41,161 samples in total.

Preprocessing steps for raw data collected from BioAI2.
The overall goal of preprocessing was to convert the raw, variable-length time-series data collected from the triboelectric sensors into a consistent and suitable format for training deep learning models, accommodating both temporal and image-based neural network inputs.

1. Data segmentation from continuous streams
The raw data collection yielded continuous, multi-channel time-series data sampled at 5000 Hz. A critical first step was to extract segments containing the relevant emotional expression actions from the background. We leveraged the fundamental physical characteristic of these actions: they all involve a “press-and-release” mechanism. This results in a distinct charge curve that initially increases and then decreases. We developed an algorithm to detect these characteristic peaks and valleys in the charge signal. The segmentation was further refined by applying constraints on the minimum and maximum permissible duration of an action, ensuring that only valid action segments were isolated for subsequent analysis.

2. Normalization and preprocessing for temporal input
For models that take raw time-series data as input, the segmented data required length normalization and value scaling. The duration of each action segment was naturally variable. To create a uniform input length of 1000 data points for the neural network, we applied spline interpolation to each segmented voltage and charge curve. This process smoothly resamples the time series to a fixed length, preserving the shape and features of the original signal without introducing significant distortion.

3. Transformation and normalization for image input
For image-based models, we transformed the 1D time-series signals into 2D image representations. We applied the CWT to the voltage waveform of each segmented action. The CWT provides a time-frequency representation (spectrogram) that reveals how the frequency content of the signal changes over time, which is often more informative than the raw temporal signal for pattern recognition. The resulting spectrogram was then converted into an RGB image, as shown in Supplementary Figure 23a(i). To enrich the input image with complementary physical information, we encoded pressure and frequency data into additional image channels. For pressure information, the maximum pressure value within an action segment was calculated. This value was then normalized by dividing by 1000, mapping it to a range suitable for an 8-bit RGB channel (0-255). This normalized value was used to create a color map that was fused with the spectrogram. For frequency information, the data was divided into windows, and the peak frequency within each window was computed. These frequency values were normalized by dividing by 2000 to fit within the RGB value range. This created another image channel representing frequency information over time. The final input image for the image models was generated by proportionally blending the base spectrogram (RGB) with the frequency (Supplementary Figure 23a (ii)) and pressure channel images (Supplementary Figure 23a(iii)), resulting in a multi-modal image that encapsulates temporal, frequency, and pressure information (Supplementary Figure 23a(iv)). Examples of these inputs are shown in Supplementary Figure 23b.

Neural network for gesture and emotion recognition. The diversity of the data from different volunteers made feature extraction and accurate recognition of the neural network more difficult. Thus, neural networks with four different settings were attempted and compared to achieve better gesture and emotion recognition. The first setting adopted the 1D CNN-LSTM hybrid network, utilizing voltage and charge as input waveforms. The other settings employed a CNN architecture (Fig. 4a (v)) and utilized images as inputs, albeit with distinct image contents. Specifically, the first one solely employed CWT images as inputs, the second one augmented frequency information based on that, and the third one incorporated both frequency and force information as shown in Fig. 4a (iii). The network was trained through leave-one-subject-out cross-validation (LOSOCV). The train set (36,739 samples) comprised data from 12 volunteers while the test set (4,422 samples) comprised data from the remaining volunteer. The stochastic gradient descent with momentum (Sgdm) algorithm was used to optimize. The initial learning rate was set to 0.001.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this paper, the author reported a biomimetic hairy affective-touch sensory AI interface (BioAI2) to bridge the gap between affective touch and effective emotion sensation inherent in the actions. Integration with hybrid neural network settings (convolutional neural network and contextual large language model) enables real-time emotion recognition (82.17% accuracy) across individualized touch patterns. Unfortunately, the manuscript contains several ambiguous statements and critical unresolved issues that currently preclude its suitability for publication.\n1. Regarding the construction of a precise mapping between action features and emotion labels, the explanation provided in the paper is insufficient and fails to convince. The authors have not adequately elaborated on the theoretical basis, methodological details, or logical reasoning underlying this mapping process.\n2. What unique applications you can achieve?\n3. What are the advantages of using ChatGPT in close loop of natural emotional interaction？\n4. Given that Transformers are highly popular in the field of natural language processing (NLP), have the authors considered incorporating them into the comparative analysis?\n5. The explanation of the preprocessing steps for the raw data collected from the sensors is insufficient. A detailed explanation of how data normalization processes were carried out is needed.\n6. There are several typos in the manuscript. And the quality of the writing needs to be improved, especially in the aspects of English expression and accuracy.", "reviewer_2": "Biomimetic hairy affective-touch sensory AI interface\nIn This work, Researchers developed a biomimetic hairy sensor that mimics human touch to help AI recognize emotions through physical contact. It generates neural-like signals without extra circuitry and detects touch with high precision. Integrated with AI models, it achieves 82% accuracy in real-time emotion recognition, paving the way for emotionally intelligent robots.I recommend the paper for minor revision, provided the following comments are adequately addressed:\nReview Comments:\n1. How is signal decoupling achieved in the case of simultaneous multi-touch inputs, especially when signals are closely spaced in time?Also What signal processing filters or algorithms are applied to detect peak timings? (Ref line: 388)\n2. How the figure (g), (h) and (k) are plotted.\n3. In Figure 3(j), the voltage vs. time graph shows multiple spikes. How does the peak detection mechanism manage variations in peak amplitude caused by inconsistent pressure or finger surface area?\n4. What test has been performed to check the homogeneity of conductive film of BioAI2 device.", "reviewer_3": "The manuscript \"Biomimetic hairy affective-touch sensory AI interface\" proposes a biomimetic, triboelectric-based tactile interface designed to recognise affective touch and enable emotionally adaptive human–machine interaction. The system integrates artificial hairy skin, neuromimetic pulse generation, deep learning-based emotion classification, and LLM-driven semantic feedback. The concept is ambitious, and the prototype demonstrates some degree of integration and engineering effort. However, the scientific foundation and experimental rigour lack sufficient depth and require significant improvement before the work can be considered for publication.\nMore specifically,\n1. Page 6-7 “Neuromimetic stroke sensing of the BioAI2 enabled by triboelectricity” I cannot find a solid theoretical model for the triboelectric pulse generation mechanism. While there is an explanation of spike generation (Fig. 2c–e), no modelling of surface potential dynamics or charge saturation is provided. These are key factors, such as charge saturation, surface potential dynamics, along with material-dependent variability, which would need to be better modelled and validated.\nFurthermore, the analogy to C-LTMRs is not sufficiently substantiated (Abstract: “...inverted U-shaped frequency-stroking speed response... resembles behavior of C-LTMRs”). While a visual comparison is made (Fig. 2g–h), no physiological or biophysical model or neural encoding principles are provided to support the claimed biomimetic equivalence.\nIt is recommended that authors consider including some additional theoretical and experimental validation of the triboelectric mechanism and neuro-mimetic claims, including some quantitative data of material physics (i.e., charge density dynamics, triboelectric charge decay, energy band alignment).\nLine 109, avoid using overstatements such as “With its exceptional lightness and flexibility,…” when no data supports these types of statements.\n2. The assertion that pulse frequencies convey emotional information is not backed by formal information-theoretic metrics. Without mutual information analysis, entropy estimation, or signal separability validation, the claim of “emotion encoding” remains speculative.\nIt is recommended that the authors provide better justification of the corresponding claims and more evidence than reported at page 6, Fig.2f and 2g caption, page 15 lines 502-503, page 18-19 lines 599-600 “...the spike frequency is positively correlated with the stroking velocity...”, and Fig.4c “Box plots of the spike frequency and exerted force under different gestures and emotions”.\n3. Pages 9-11, Fig. 3g-i. The isoline-based localisation method is promising but mathematically underdeveloped in the main text. Assumptions like uniform resistance and infinite boundaries are idealised and remain not fully validated against actual fabrication tolerances. Despite the supplementary notes 1-2 and supplementary Figs. 3-8, the isoline derivation method assumes infinite uniform resistor grid and lacks quantitative analysis of real-world fabrication variation. It is recommended that the authors provide quantitative data on conductivity variation and edge effects.\n4. Pages 14. Emotional labels. Labels were obtained via self-assessment alone, without cross-checking with physiological sensors, which introduces subjective bias. I could have been worth including some well-recognised physiological validation (e.g., GSR, ECG) to corroborate emotional states. Moreover, gesture force and velocity are confounded in the pulse output (Fig.4c)—without controlled mechanical stimulation, feature attribution remains uncertain.\n5. Page 13-15, Fig. 4a-d. The selection of neural networks used (1D CNN-LSTM for gestures, standard CNN for emotion) is not fully justified. For instance, there is no comparison with alternative architectures (e.g., Transformers, ResNet, TCNs), nor are ablation studies presented to validate their effectiveness. The model's interpretability is also lacking—no insights are provided into what features drive classification decisions. It is suggested that the authors consider adding to the supplementary Fig. 9-12 some cross-architecture comparison and ablation studies.\n6. It is also recommended to include model interpretability data and information such as Grad-CAM, saliency maps, or attention weights, to inform what features the CNN attends to for classification, for instance.\n7. Page 16-18 “Close loop of natural emotional interaction integrated with Chat Generative Pre-trained Transformer (ChatGPT)” and supplementary Fig. 14-15. Please provide quantitative analysis and comparison of the selected transformer network model with traditional dialogue systems or user studies.\n8. The manuscript is affected by several typos and language errors. It is recommended to proofread it before resubmission.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response to reviewer:\nWe are delighted to hear your valuable feedback on our study. Your comments have been extremely\nhelpful in guiding us towards enhancing the quality of our work. We have carefully reviewed each of\nyour suggestions and have made detailed modifications accordingly. The revised version now presents a\nclearer and more coherent narrative, which we believe will greatly enhance the reader’s understanding\nof our research. We have made point-by-point replies as follows:\nComment 1: Regarding the construction of a precise mapping between action features and emotion labels,\nthe explanation provided in the paper is insufficient and fails to convince. The authors have not\nadequately elaborated on the theoretical basis, methodological details, or logical reasoning underlying\nthis mapping process.\nResponse to reviewer:\nWe sincerely thank you for raising this important point regarding the mapping between action features\nand emotion labels. Below we provide a more detailed elaboration on the theoretical foundations,\nmethodological design, and logical reasoning supporting this mapping process.\nTheoretical basis:\nA growing body of literature has established strong correlations between tactile action features and\nemotional experiences. From a neurophysiological perspective, affective touch is specifically mediated\nby C-low-threshold mechanoreceptors (C-LTMRs). In particular, MRGPRB4+ neurons—which are\nselectively expressed in C-LTMRs—have been shown to respond to massage-like stroking but not to\nnoxious mechanical stimuli. Moreover, optogenetic activation of these neurons induced conditioned\nplace preference in mice, suggesting their role in positive affective processing1. This provides a\nneurobiological basis for linking specific tactile actions (e.g., gentle stroking) to positive emotional states.\nFurthermore, multiple psychophysical studies support the relationship between tactile kinematics and\nemotional dimensions (valence and arousal). For example, Teyssier et al. demonstrated that variations in\ntouch velocity (3.8, 16 cm/s) and force (0.3, 1.2 N) significantly modulate emotional valence and arousal2.\nSimilarly, Hertenstein et al. and McIntyre et al. showed that humans can accurately decode distinct\nemotions—including anger, fear, love, and gratitude—via touch alone, confirming that tactile actions\ncarry emotionally relevant information3,4.\nFig. R1 a Illustration of stroking the human skin and the spike frequency of nerve output from the C-\nLTMRs and the corresponding rating of pleasantness under different stroking speeds, which\ndemonstrate their positive correlation. The data is redrawn based on the result from the work of\nAckerley et al.25 (CC BY-NC-SA). b Illustration of stroking the BioAI2 and the relationship between\nspike frequency of BioAI2 and stroking speed under different d. c Relationship between exerted forces\nand calculated charge output under different designs of the hair (same height and different height),\nwhere the result shows that the hair with two-stage heights exhibits higher sensitivity.\nMethodological details:\nOur artificial hair sensor was specifically designed to capture tactile features associated with C-LTMR\nresponses. As shown in Fig. R1a–b (original Fig. 2g–h), the sensor’s spiking frequency varies with\nstroking speed in a manner analogous to biological C-LTMRs. Additionally, its force sensitivity (Fig.\nR1c, original Fig. 3d) covers the typical range of human affective touch (e.g., gentle stroking or pressing).\nThese biomimetic characteristics ensure that the sensor reliably encodes key kinematic parameters (speed,\nforce, location) relevant to emotional communication.\nTo establish a robust mapping from sensor outputs to emotion labels, we conducted controlled subjective\nexperiments involving 13 participants. Each tactile gesture was accompanied by real-time emotional\nratings using the Self-Assessment Manikin (SAM)5—a widely adopted instrument for assessing valence\nand arousal6-9. This design ensures that the recorded sensor data correspond to ground-truth emotional\nlabels. A neural network was then trained on this dataset to learn the complex, non-linear relationship\nbetween tactile features and emotional dimensions.\nLogical reasoning:\nThe mapping process is built on the following logic:\n1. Neurophysiological and behavioral evidence confirms that specific tactile features (e.g., low\nforce, moderate velocity) elicit consistent emotional responses.\n2. Our sensor replicates the encoding properties of biological affective touch receptors.\n3. By collecting synchronized tactile-action and emotion-label data under controlled conditions,\nwe align sensor outputs with psychologically validated emotional states.\n4. The neural network serves as a data-driven function approximator that generalizes the feature–\nemotion relationship beyond the training set.\nWe apologize for the insufficient explanation in the original manuscript and hope this clarification\nadequately addresses the reviewer’s concerns. We will incorporate these points into the revised paper to\nimprove clarity and rigor.\nMeanwhile, the corresponding modification has been highlighted in our revised manuscript. Specifically,\nthe details are shown below and in Supplementary Note 6:\nFrom page 17, line 15 to page 18, line 13: “ Because of its excellent multi-dimensional tactile perception\ncapability, the BioAI2 effectively captures the spatiotemporal characteristics of touch, enabling a deeper\nunderstanding of its affective meaning. It is important to note that the same gesture can convey vastly\ndifferent emotions depending on the context. Therefore, interpreting affective touch should rely not on\ngesture type alone, but on detailed action characteristics. To explore this, we conduct an experiment to\ninvestigate the features of eight representative gestures (Fig. 4e) under different emotional states. A\ncritical challenge in such studies is constructing an accurate mapping between these low-level action\nfeatures and discrete emotion labels. The pursuit of this mapping is grounded in growing evidence linking\ntactile kinematics to emotional experience (Details are shown in Supplementary Note 6). From a\nneurophysiological perspective, specific tactile action features, such as velocity and force, have been\nshown to modulate emotional responses, mediated by specialized afferent pathways23. Furthermore,\npsychophysical studies showed that humans can accurately decode distinct emotions—including anger,\nfear, love, and gratitude—via touch alone, confirming that tactile actions carry emotionally relevant\ninformation56,57. This provides a solid theoretical basis for establishing a quantifiable relationship\nbetween how a touch is performed and the emotion it conveys.”\nComment 2: What unique applications you can achieve?\nResponse to reviewer:\nThis is an important question. Actually, affective touch is deeply embedded in various aspects of human\nlife, leading to its wide range of potential applications.\nEarly-life care & parental guidance\nAffective touch plays an indispensable role in infant development and the formation of parent-child\nrelationships. From birth, infants rely on skin-to-skin contact—particularly nurturing touches and hugs\nfrom the mother—to perceive safety, build trust, and support the healthy development of their early\nnervous system. However, many parents, especially first-time caregivers, have a limited ability to\naccurately interpret the emotional signals conveyed through an infant’s tactile behavior. Therefore,\ndeveloping technology that can objectively sense and decode infants’ affective touch signals may offer\nparents scientifically grounded insights—helping them better distinguish between touch behaviors\ntriggered by hunger, fatigue, or the need for comfort—and respond in a more timely and appropriate\nmanner.\nMental health therapy and intervention\nUnder significant societal pressure, many adolescents, young adults, and even middle-aged and older\nadults are suffering from autism, affective disorders, depression, and other psychological conditions.\nAffective touch has been demonstrated in numerous studies to be an effective tool for healing mental\nhealth10,11. It can enhance social connections and promote intimate relationships. Therefore, developing\neffective affective touch sensing devices is highly beneficial for providing technological solutions to treat\nthese psychological problems.\nNatural human-robot interaction for social companionship\nService robots integrated with affective touch perception possess superior natural interaction capabilities.\nThe affective touch sensing ability would endow robot the capability of sensing the emotion of human,\nand thus better understand people’s social behavior. On this basis, the robot can respond more humanly\nto human behavior and provide a warm feeling. This is particularly promising for mitigating loneliness\namong children left at home and solitary elderly individuals in the future service robot industry.\nI hope this elaborated response adequately addresses the reviewer’s inquiry. Should further clarification\nor specific details be required, I am happy to provide them.\nMeanwhile, the corresponding modification has been highlighted in our revised manuscript. Specifically,\nthe details are shown below:\nFrom page 25, line 43 to page 26, line 3: “Through its ability to decode the emotional signals embedded\nin touch, the BioAI2 shows considerable potential across key domains. It can provide scientifically\ngrounded insights for infant care, serve as a supportive tool in mental health interventions, and facilitate\nmore natural and empathetic human-robot interactions, thereby bridging advanced technology with\nessential human requirements for well-being.”\nComment 3: What are the advantages of using ChatGPT in close loop of natural emotional interaction？\nResponse to reviewer:\nWe appreciate your insightful question regarding the advantages of using ChatGPT in a closed-loop\nnatural emotional interaction system. The key advantage lies in its advanced reasoning capability, which\nenables context-aware understanding and appropriate response generation—particularly crucial for\naffective touch interpretation, where raw sensor data alone is insufficient.\nUnlike simple mapping approaches, affective touch is highly context-dependent12. Neuroimaging studies\nhave shown that neural responses to social touch in regions such as the amygdala, orbitofrontal cortex,\nand somatosensory cortices are modulated by interpersonal closeness13. Meanwhile, the personality traits,\ngender and interaction scenario are demonstrated to have high impact on the communication of emotion\nvia touch14,15. This means that accurately interpreting touch and generating an appropriate emotional\nresponse requires deep situational understanding. ChatGPT excels in this regard by incorporating\ncontextual cues into its inference process, allowing it to adapt responses based on factors such as the\ninteraction setting, user identity, and emotional history. As demonstrated in Supplementary Figure 15,\nChatGPT dynamically adjusts its feedback to align with specific scenarios, ensuring relevance and\ninterpersonal appropriateness.\nFig. R2 Natural emotional interaction processes executed by the proposed system under two\ndifferent scenarios (going to work and coming home).\nFurthermore, with the development of researchers, GPT has become increasingly capable of\ncomprehension through dialogue, and can also stably output texts in line with the current emotional state\nbased on the emotions of understanding16. This ability is coupled with consistent and emotionally\ncongruent text generation, making GPT highly suitable for real-time emotional interaction loops. In our\nwork, this effective emotional independent decision-making ability of GPT is used to allow GPT to select\nthe robot dog movements that are most in line with the current emotional state (Fig. R2, original Fig. 5e)\nto provide the most appropriate emotional feedback to the house owner.\nComparison between ChatGPT assisted BioAI2 interaction system to traditional interaction system:\nUnlike traditional rule-based or simple classifier-based systems, GPT demonstrates a remarkable ability\nto grasp nuance, sarcasm, and context. In our system, this translates to the AI understanding the emotional\nintent behind a user’s action based on the specific scenario. As illustrated in Fig. R3, the same physical\ninteraction (e.g., a tickle) can be interpreted differently by ChatGPT depending on the context (e.g., first\nmeet vs. stay for a long time), leading to diverse and appropriate emotional feedback from the robotic\ndog. This flexibility is a cornerstone of natural interaction.\nFig. R3. Flowchart illustrating the impact of the context information on the understanding and\nexpression of emotions. Here, two different context information (scenario, characteristics of the dog,\nand the relationship between human and dog) were set and the same interaction (tickle) was applied to\nthe BioAI2. The ChatGPT presented different emotion understanding and executed different actions to\nexpress different emotions.\nFig. R4 Chord diagram describing the likelihood of different dog actions chosen by ChatGPT\nunder different emotion states of humans.\nTraditional systems often rely on a one-to-one mapping of input to a pre-defined output, making\ninteractions predictable and rigid. In contrast, ChatGPT’s language understanding enables it to generate\nintelligent, autonomous decisions on how to respond emotionally. This allows for unpredictable, lifelike,\nand more engaging communication, moving beyond stale, programmatic interactions. Specifically, the\nrobot dog can utilize any combination of its 10 distinct actions (Fig. R4, original Fig. 5d) for emotional\nfeedback. Even when limiting the number of actions per response to three, the system can produce up to\n10 (for one action) + 109 (for two actions) + 109×8 (for three actions) = 820 distinct combinations,\nensuring significant variability and unpredictability. As summarized in Table R1, which compares several\ne-skin-based emotional HRI works, our ChatGPT-based robot dog demonstrates the highest level of\nintelligence by effectively contextualizing information and diversifying response types.\nTable R1 E-skin based human-robot interaction\nIf consider\nInteraction If detect Possible types\nWorks contextual Decision making\nobjects emotion of responses\ninformation\nProgram\nDu et al.34 Robot car Yes No 6\npresetting\nRobot dog\nProgram\nHuang et al.45 and virtual Yes No 7\npresetting\ncharacter\nProgram\nChoi et al.40 Virtual robot Yes No 4\npresetting\nVirtual Program\nLee et al.32 Yes Yes N/A\ncharacter presetting\nChatGPT-based\nThis work Robot dog Yes Yes independent >820\ndecision making\nIn summary, ChatGPT provides a significant advantage in closing the loop of natural emotional\ninteraction through its nuanced context reasoning, and flexible affective expression generation—\ncapabilities that are essential for handling the complexity of real-world affective touch communication.\nMeanwhile, the corresponding modification has been highlighted in our revised manuscript. Specifically,\nthe details are shown below:\nOn page 23, lines 17 to 31: “Specifically, the ChatGPT is integrated into this emotional interaction\nframework primarily due to its advanced reasoning capability, which enables context-aware\nunderstanding and appropriate response generation. It excels by dynamically incorporating contextual\ncues into its inference process, adapting responses based on the interaction setting, user identity, and\nemotional history to ensure relevance and interpersonal appropriateness. Moreover, its ability to\ncomprehend through dialogue and generate stable, emotionally congruent text makes it highly suitable\nfor real-time emotional interaction loops, supporting consistent affective expression. Compared to other\nTransformer architectures, such as BERT, which excels in comprehension tasks like sentiment\nclassification when fine-tuned on specific datasets, GPT provides distinct advantages in zero-shot and\nfew-shot scenarios where extensive task-specific training is not feasible66,67 (Supplementary Note 7).\nThis robust few-shot and zero-shot learning capability, stemming from its generative pre-training and\nautoregressive design, makes GPT particularly suitable for the dynamic and evolving context of natural\nemotional interaction proposed in our study. The entire process has been depicted in a flowchart as shown\nin Fig. 6b.”\nAnd from page 24, line 28 to page 25, line 5: “The integration of ChatGPT is pivotal for achieving natural\nemotional interaction, primarily due to its superior contextual understanding and flexible decision-\nmaking capabilities, which distinguish it from traditional rule-based or simple classifier-based systems.\nChatGPT demonstrates a remarkable ability to grasp nuance and interpret emotional intent based on the\nspecific scenario. For example, we implement another experiment that applied the same interaction\ngesture (tickle) on the BioAI2 without considering the emotional information extracted from the gesture\nwas also performed (as shown in Supplementary Figure 32 and Supplementary Video 4). The ChatGPT\npresents a completely different emotion understanding and execute diverse motion feedback under\ndifferent scenarios, characteristics of the dog and their relationship. The result indicates the significant\nimpact of contextual information on ChatGPT’s decision during emotional interaction. Unlike systems\nthat rely on a one-to-one mapping of input to a pre-defined output, resulting in predictable and rigid\ninteractions, ChatGPT’s language understanding enables it to generate intelligent, autonomous decisions\non how to respond emotionally. This allows for unpredictable, lifelike, and more engaging\ncommunication, moving beyond stale, programmatic interactions. Specifically, the robot dog can utilize\nany combination of its 10 distinct actions (Fig. 6d) for emotional feedback. Even when limiting the\nnumber of actions per response to three, the system can produce up to 10 (for one action) + 109 (for two\nactions) + 109×8 (for three actions) = 820 distinct combinations, ensuring significant variability and\nunpredictability. As summarized in Supplementary Table 6, which compares several e-skin-based\nemotional HRI works, our ChatGPT-based robot dog demonstrates the highest level of intelligence by\neffectively contextualizing information and diversifying response types.”\nComment 4: Given that Transformers are highly popular in the field of natural language processing (NLP),\nhave the authors considered incorporating them into the comparative analysis?\nResponse to reviewer:\nWe thank the reviewer for raising this important point regarding the inclusion of Transformer-based\nmodels in our comparative analysis. We acknowledge the significance of Transformer architectures,\nespecially models like BERT, in natural language processing tasks, particularly for sentiment analysis.\nWhile sentiment analysis capabilities of Transformers and GPT were not the primary focus of our\nresearch direction, empirical evidence from existing literature supports their strong performance in\nsentiment understanding. Specifically, BERT and other Transformer-based models, when fine-tuned on\ndedicated datasets, can achieve high accuracy in specific task scenarios. In contrast, models like GPT\noffer distinct advantages in zero-shot or few-shot sentiment analysis without requiring extensive task-\nspecific fine-tuning.\nFor instance, David et al. conducted a comparative analysis using a database of 30,000 COVID-19-\nrelated tweets to evaluate the sentiment analysis and emotion classification capabilities of GPT-3.5 and\nIBM Watson17. Their findings indicate that GPT-3.5’s contextual prompting approach enables it to better\ngrasp nuanced elements such as sarcasm, despite not being explicitly trained for sentiment analysis.\nFurther supporting this, Jan et al. evaluated the performance of high-performance transfer learning\nmodels like RoBERTa against state-of-the-art LLMs—including GPT-3.5, GPT-4, and Llama 2—in zero-\nshot, three-class sentiment classification tasks on a dataset of 792 text documents from Amazon,\nFlipkart, and Twitter18. Their results showed that GPT-4 achieved the highest classification accuracy in three out\nof four datasets, with an average performance at least 4.9 percentage points higher than other LLMs and\nRoBERTa. While RoBERTa, fine-tuned on a corpus containing over 5,000 social media posts, excelled\nin one specific Twitter dataset, highlighting the advantage of model specialization, the overall strong\nzero-shot performance of LLMs like GPT-4 is evident.\nTable R2 presents the classification accuracy of several models in the three-class sentiment classification\nexperiment. Overall, GPT-4 achieved the highest classification accuracy in three out of four datasets,\nwith an average performance at least 4.9 percentage points higher than other LLMs and RoBERTa. GPT-\n3.5 and RoBERTa showed similar results, each recording an average accuracy of approximately 78%.\nRoBERTa, which was specifically fine-tuned on a corpus containing over 5,000 social media posts,\ndemonstrated the highest accuracy in one of the Twitter datasets (see Table R1, Twitter Various II). This\nenhanced performance can be attributed to its training on data similar to the tested Twitter dataset,\nhighlighting the advantage of model specialization. The experimental results underscore the outstanding\nzero-shot performance of LLMs in sentiment analysis. However, they also emphasize that specialized,\nfine-tuned transfer learning models like RoBERTa can outperform LLMs in certain scenarios.\nTable R2 Three-class sentiment classification accuracy for RoBERTa and LLMs18\nTransfer Learning\nLLMs (zero-shot)\nModel (fine-tuned)\nRoBERTa GPT-3.5 GPT-4 Llama 2\nAmazon (iPhone) 73.2 85.4 88.9 87.9\nFlipkart (Various) 78.3 91.9 92.4 90.9\nTwitter (Airlines II) 72.7 69.2 77.8 67.7\nTwitter (Various II) 86.4 60.1 71.2 56.1\nAverage 77.7 77.7 82.6 75.7\nAdditionally, without additional training, Jeana et al. compared a lexicon-based model (VADER), a deep\nlearning Transformer-based model (Twitter-roBERTa-base), and a GPT-based model (GPT-4o mini) for\nsentiment analysis on an energy transition-related tweet corpus containing 1,000 manually annotated\nEnglish tweets19. As shown in Table R3, the GPT-based model outperformed the other two approaches,\nfurther underscoring the high performance of GPT in zero-shot settings.\nTable R3 Performance comparison across different sentiment classification models19\nAccuracy\nModel Precision (%) Recall (%) F1-score (%)\n(%)\nVADER (Lexicon-based) 65 (92, 52, 64) (27, 35, 93) (42, 42, 76)\nTwitter-RoBERTa-Base 76 (97, 48, 93) (52, 85, 84) (67, 62, 88)\nGPT-4o mini 83 (99, 82, 78) (87, 38, 100) (92, 52, 88)\nIn summary, while specialized, fine-tuned Transformer models like BERT excel in specific understanding\ntasks, GPT’s robust zero-shot and few-shot learning capabilities make it particularly suitable for the\napplication scenario proposed in our study, where extensive task-specific training is not feasible.\nWe appreciate the reviewer’s insightful comment and will consider a more detailed discussion of\nTransformer model comparisons as an important direction for future work.\nMeanwhile, the corresponding modification has been highlighted in our revised manuscript. Specifically,\nthe details are shown below and Supplementary Note 7:\nOn page 23, lines 17 to 31: “Specifically, the ChatGPT is integrated into this emotional interaction\nframework primarily due to its advanced reasoning capability, which enables context-aware\nunderstanding and appropriate response generation. It excels by dynamically incorporating contextual\ncues into its inference process, adapting responses based on the interaction setting, user identity, and\nemotional history to ensure relevance and interpersonal appropriateness. Moreover, its ability to\ncomprehend through dialogue and generate stable, emotionally congruent text makes it highly suitable\nfor real-time emotional interaction loops, supporting consistent affective expression. Compared to other\nTransformer architectures, such as BERT, which excels in comprehension tasks like sentiment\nclassification when fine-tuned on specific datasets, GPT provides distinct advantages in zero-shot and\nfew-shot scenarios where extensive task-specific training is not feasible66,67 (Supplementary Note 7).\nThis robust few-shot and zero-shot learning capability, stemming from its generative pre-training and\nautoregressive design, makes GPT particularly suitable for the dynamic and evolving context of natural\nemotional interaction proposed in our study. The entire process has been depicted in a flowchart as shown\nin Fig. 6b.”\nComment 5: The explanation of the preprocessing steps for the raw data collected from the sensors is\ninsufficient. A detailed explanation of how data normalization processes were carried out is needed.\nResponse to reviewer:\nWe sincerely thank the reviewer for pointing out the need for a more detailed explanation of our data\npreprocessing steps. We apologize for the insufficient description in the original manuscript. Below, we\nprovide a comprehensive elaboration on the preprocessing and normalization procedures applied to the\nraw sensor data, which we will incorporate into the revised manuscript to enhance clarity and\nreproducibility.\nThe overall goal of preprocessing was to convert the raw, variable-length time-series data collected from\nthe triboelectric sensors into a consistent and suitable format for training deep learning models,\naccommodating both temporal and image-based neural network inputs.\n1. Data segmentation from continuous streams\nThe raw data collection yielded continuous, multi-channel time-series data sampled at 5000 Hz. A critical\nfirst step was to extract segments containing the relevant emotional expression actions from the\nbackground. We leveraged the fundamental physical characteristic of these actions: they all involve a\n\"press-and-release\" mechanism. This results in a distinct charge curve that initially increases and then\ndecreases. We developed an algorithm to detect these characteristic peaks and valleys in the charge signal.\nThe segmentation was further refined by applying constraints on the minimum and maximum permissible\nduration of an action, ensuring that only valid action segments were isolated for subsequent analysis.\n2. Normalization and preprocessing for temporal input\nFor models that take raw time-series data as input, the segmented data required length normalization and\nvalue scaling. The duration of each action segment was naturally variable. To create a uniform input\nlength of 1000 data points for the neural network, we applied spline interpolation to each segmented\nvoltage and charge curve. This process smoothly resamples the time series to a fixed length, preserving\nthe shape and features of the original signal without introducing significant distortion.\n3. Transformation and normalization for image input\nFor image-based models, we transformed the 1D time-series signals into 2D image representations.\nWe applied the CWT to the voltage waveform of each segmented action. The CWT provides a time-\nfrequency representation (spectrogram) that reveals how the frequency content of the signal changes over\ntime, which is often more informative than the raw temporal signal for pattern recognition. The resulting\nspectrogram was then converted into an RGB image, as shown in Fig. R5a(i).\nTo enrich the input image with complementary physical information, we encoded pressure and frequency\ndata into additional image channels. For pressure information, the maximum pressure value within an\naction segment was calculated. This value was then normalized by dividing by 1000, mapping it to a\nrange suitable for an 8-bit RGB channel (0-255). This normalized value was used to create a color map\nthat was fused with the spectrogram. For frequency information, the data was divided into windows, and\nthe peak frequency within each window was computed. These frequency values were normalized by\ndividing by 2000 to fit within the RGB value range. This created another image channel representing\nfrequency information over time.\nThe final input image for the image models was generated by proportionally blending the base\nspectrogram (RGB) with the frequency (Fig. R5a(ii)) and pressure channel images (Fig. R5a(iii)),\nresulting in a multi-modal image that encapsulates temporal, frequency, and pressure information (Fig.\nR5a(iv)). Examples of these input are shown in Fig. R5b.\nFig. R5 Illustration of the process methods of the image input. a There are four types of image\ninputs for the neural network. (i) The first type of image is obtained through the continuous wavelet\ntransform of voltage signals to extract the temporal information from data. (ii) The second input\nconsists of the CWT result and the magnitudes of the spike frequency which are calculated every 1 s\nand are rendered as different colors at the top part of the image. (iii) The third input adds the amplitude\nof the force to the first input. (iv) The fourth input integrates multimodal information (temporal,\nfrequency and pressure information). b Some samples of image processed by the above methods.\nMeanwhile, the corresponding modification has been highlighted in our revised manuscript. Specifically,\nthe details are shown below:\nFrom page 27, line 10 to page 28, line 5: “ The overall goal of preprocessing was to convert the raw,\nvariable-length time-series data collected from the triboelectric sensors into a consistent and suitable\nformat for training deep learning models, accommodating both temporal and image-based neural network\ninputs.\n1. Data segmentation from continuous streams\nThe raw data collection yielded continuous, multi-channel time-series data sampled at 5000 Hz. A critical\nfirst step was to extract segments containing the relevant emotional expression actions from the\nbackground. We leveraged the fundamental physical characteristic of these actions: they all involve a\n“press-and-release” mechanism. This results in a distinct charge curve that initially increases and then\ndecreases. We developed an algorithm to detect these characteristic peaks and valleys in the charge signal.\nThe segmentation was further refined by applying constraints on the minimum and maximum permissible\nduration of an action, ensuring that only valid action segments were isolated for subsequent analysis.\n2. Normalization and preprocessing for temporal input\nFor models that take raw time-series data as input, the segmented data required length normalization and\nvalue scaling. The duration of each action segment was naturally variable. To create a uniform input\nlength of 1000 data points for the neural network, we applied spline interpolation to each segmented\nvoltage and charge curve. This process smoothly resamples the time series to a fixed length, preserving\nthe shape and features of the original signal without introducing significant distortion.\n3. Transformation and normalization for image input\nFor image-based models, we transformed the 1D time-series signals into 2D image representations. We\napplied the CWT to the voltage waveform of each segmented action. The CWT provides a time-\nfrequency representation (spectrogram) that reveals how the frequency content of the signal changes over\ntime, which is often more informative than the raw temporal signal for pattern recognition. The resulting\nspectrogram was then converted into an RGB image, as shown in Supplementary Figure 23a(i). To enrich\nthe input image with complementary physical information, we encoded pressure and frequency data into\nadditional image channels. For pressure information, the maximum pressure value within an action\nsegment was calculated. This value was then normalized by dividing by 1000, mapping it to a range\nsuitable for an 8-bit RGB channel (0-255). This normalized value was used to create a color map that\nwas fused with the spectrogram. For frequency information, the data was divided into windows, and the\npeak frequency within each window was computed. These frequency values were normalized by dividing\nby 2000 to fit within the RGB value range. This created another image channel representing frequency\ninformation over time. The final input image for the image models was generated by proportionally\nblending the base spectrogram (RGB) with the frequency (Supplementary Figure 23a (ii)) and pressure\nchannel images (Supplementary Figure 23a(iii)), resulting in a multi-modal image that encapsulates\ntemporal, frequency, and pressure information (Supplementary Figure 23a(iv)). Examples of these input\nare shown in Supplementary Figure 23b.”\nComment 6: There are several typos in the manuscript. And the quality of the writing needs to be\nimproved, especially in the aspects of English expression and accuracy.\nResponse to reviewer:\nWe thank the reviewer for this important observation and for the careful review of our manuscript. We\nsincerely apologize for the typos and the lack of clarity in the English expression, which affected the\nquality of the initial submission. We have conducted a thorough, line-by-line proofreading of the entire\nmanuscript to identify and correct all typographical errors. All changes have been implemented in the\nrevised manuscript, which we believe now meets the high standards of academic writing required for\npublication.\nReviewer #2: Comments to Author\nIn this work, Researchers developed a biomimetic hairy sensor that mimics human touch to help AI\nrecognize emotions through physical contact. It generates neural-like signals without extra circuitry and\ndetects touch with high precision. Integrated with AI models, it achieves 82% accuracy in real-time\nemotion recognition, paving the way for emotionally intelligent robots. I recommend the paper for minor\nrevision, provided the following comments are adequately addressed:\nMajor Comments\nResponse to reviewer:\nThank you for your thorough review and valuable comments on our manuscript. These questions are\nindeed crucial and play a significant role in enhancing the clarity and presentation of our manuscript. We\nhave thoroughly considered your suggestions and made corresponding replies and revisions. We hope\nthat these responses will adequately address the issues you have raised. If there have any further questions\nor concerns, we would greatly appreciate your continued in-depth communications with us. Once again,\nthank you for your insightful comments and suggestions.\n1. How is signal decoupling achieved in the case of simultaneous multi-touch inputs, especially when\nsignals are closely spaced in time? Also, what signal processing filters or algorithms are applied to detect\npeak timings? (Ref line: 388).\nResponse to reviewer:\nWe thank the reviewer for this insightful question regarding the critical aspects of signal decoupling and\nprocessing in multi-touch scenarios. Our approach leverages the unique high-temporal-resolution\ncharacteristics of the BioAI2 interface, combined with specific signal processing techniques, to\neffectively distinguish closely spaced inputs. Below we provide a detailed explanation.\n1. High Temporal Resolution of BioAI2 Signals for Intrinsic Decoupling\nThe foundation of our signal decoupling capability lies in the inherently high temporal resolution of the\ntriboelectric pulses generated by the BioAI2 hairy structure. As illustrated in Fig. R6, a comparison with\na traditional plane interface reveals a significant difference: while the contact signal from the plane\ninterface can last up to 50 ms with frequency components primarily below 500 Hz, the pulse signal from\nBioAI2 is extremely brief, lasting only about 2 ms, with its high-frequency components reaching up to\nshort pulse duration creates a distinct temporal separation between consecutive events. Even if two finger\ncontacts occur in very close succession (e.g., with an interval of less than 20 ms, as demonstrated in Fig.\nR6), the individual spike-like signals remain temporally distinct. This high temporal resolution allows\nthe system to resolve inputs that would otherwise overlap and become indistinguishable using\nconventional interfaces, providing an intrinsic physical-layer mechanism for decoupling.\nFig. R6 Illustration of the triboelectric signals and corresponding time-frequency diagram\ngenerated from (a) a traditional plane interface and (b) the proposed BioAI2.\n2. Signal Processing: Bandpass Filtering for Peak Detection\nAlthough the high-frequency pulses are naturally separable, their low-frequency components can overlap\nwhen two pulses occur in close succession, potentially leading to misinterpretation if not processed\ncorrectly (as conceptually shown in the upper part of Fig. R7(ii)). To isolate the decisive high-frequency\ncomponents and accurately identify the timing of each pulse, we apply a bandpass filter with a passband\nof 50 Hz to 600 Hz. The choice of this frequency range is deliberate: it effectively attenuates the slow-\nvarying low-frequency baseline drift that can cause interference, while preserving the essential high-\nfrequency content that defines the sharp, spike-like nature of our signals. The result of this filtering\nprocess is shown in the lower part of Fig. R7(ii), where the clean, isolated peaks are clearly visible after\nthe low-frequency interference has been removed. This step is vital for reliable peak detection.\nFig. R7 Single finger stroking trajectory positioning ability. (i) First, collect the sliding data; (ii)\nSecond, in order to avoid the interference of the low-frequency signal, the bandpass filter is carried out,\nand then the signal pulse peak is found; (iii) Third, after finding suitable peaks on four signals, the\nratios in Equation (3) and (4) are then calculated; (iv) In the fourth step, the stroking trajectory points\nare obtained based on the ratios and the isoline theory. According to the result, attributed to the super-\nhigh temporal resolution of the spike-like signals, the trajectory points from a single finger can be\nreconstructed continuously.\n3. Peak Detection and Multi-Touch Coordination\nFollowing bandpass filtering, a peak detection algorithm is employed to identify the precise timing of\neach pulse across the four sensor channels. The algorithm searches for local maxima that exceed a\npredetermined threshold within the filtered signal. The high signal-to-noise ratio of the filtered pulses\nmakes this detection robust. For multi-touch scenarios, as exemplified in Fig. R8, when two fingers\ngenerate pulses separated by less than 20 ms, the combination of the intrinsic pulse brevity and the\nbandpass filtering ensures that two adjacent peaks are clearly resolved without influencing each other.\nOnce the peaks for a specific touch event are identified on all four channels, the ratios calculated from\ntheir amplitudes (using Equation (3) and (4) from the manuscript) are used to determine the touch\ncoordinates via the isoline theory. The process successfully calculates distinct coordinates for each finger,\nas shown by the results (3.7, 11.0) and (2.3, 4.6) in Fig. R8(iv).\nIn summary, signal decoupling for simultaneous multi-touch inputs in BioAI2 is achieved through a\nsynergistic effect: the hardware generates intrinsically short, high-frequency pulses that provide natural\ntemporal separation, and the signal processing chain (bandpass filtering and peak detection) cleanly\nisolates these pulses for accurate timing determination and coordinate calculation.\nFig. R8 Multi-finger stroking trajectory positioning ability. (i) First, collect the sliding data; (ii)\nSecond, in order to avoid the interference of the low-frequency signal, the bandpass filter is carried out,\nand then the signal pulse peak is found; (iii) Third, after finding suitable peaks on four signals, the\nratios in Equation (3) and (4) are then calculated; (iv) In the fourth step, the stroking trajectory points\non the two fingers are obtained based on the ratios and the isoline theory. According to the result,\nattributed to the super-high temporal resolution of the spike-like signals, the trajectory points from two\nfingers will not influence each other.\nMeanwhile, the corresponding modification has been highlighted in our revised manuscript. Specifically,\nthe details are shown below and Supplementary Note 5:\nOn page 14, lines 16 to 26: “As shown in Supplementary Figure 15c(ii), The exceptionally brief duration\n(<2 ms) of these spike-like pulses, contrasting with the prolonged signals from traditional plane interfaces,\nprovides intrinsic temporal separation for decoupling simultaneous or near-simultaneous touches. Its\ntime-frequency diagram presents significant high-frequency components compared to that of the plane\ninterface. Attributing to these components, the multiple touches can be fully reconstructed by comparing\nthe ratios of peaks using the isoline theory. As depicted in Fig. 3m, the super-high temporal resolution\nof the structure enables effective distinction between signals (peak ① and peak ②) generated in a very\nshort time. Attributing to the high temporal resolution, continuous and even the two-finger sliding\ntrajectory are effectively reconstructed (Fig. 3n, Supplementary Figure 18-19, further details are\ndiscussed in Supplementary Note 5).”\n2. How the figure 3 (g), (h) and (k) are plotted.\nResponse to reviewer:\nWe sincerely apologize for the lack of clarity in our original description of Fig. 3(g), (h), and (k). We\nappreciate the opportunity to provide a detailed explanation of the plotting methodology for these figures.\n1. Explanation of Fig. 3(g): isoline plots of ratios x and y\nFigure 3(g) presents isoline plots of two ratios, x and y, which are calculated using the following formulas\nderived from the peak voltages (V₁, V₂, V₃, V₄) measured at the four electrodes:\n1 1\n+\n𝑉 +𝑉 𝑅 𝑅\n𝑥 = 1 2 ≈ 1 2 (𝑅1)\n𝑉 +𝑉 +𝑉 +𝑉 1 1 1 1\n1 2 3 4 + + +\n𝑅 𝑅 𝑅 𝑅\n1 2 3 4\n1 1\n+\n𝑉 +𝑉 𝑅 𝑅\n𝑦= 1 4 ≈ 1 4 (𝑅2)\n𝑉 +𝑉 +𝑉 +𝑉 1 1 1 1\n1 2 3 4 + + +\n𝑅 𝑅 𝑅 𝑅\n1 2 3 4\nThe plotting procedure was as follows:\nFirstly, we calculated the values of ratio x for every point across the entire X-Y plane based on the\ntheoretical model (Equation (R1) and (R3)):\n𝑅 𝑥 𝑦\n1\nlim = + +\n𝑀,𝑁→∞ 𝑟 𝑀 𝑁\n1 1 𝜃 𝜑 2\n1 𝜋 𝜋{𝑐𝑜𝑠[(𝑥+ 2 )𝜃]𝑐𝑜𝑠[(𝑦+ 2 )𝜑]−𝑐𝑜𝑠 2 𝑐𝑜𝑠 2 }\n∫ ∫ 𝑑𝜃𝑑𝜑 (𝑅3)\n𝜋2 sin2𝜃+sin2𝜑\n0 0\nSecondly, using scientific plotting software (MATLAB), we generated the isolines for ratio x. Each\nisoline (represented by red curves in Fig. R9) connects all points sharing the same value of x. The color\nintensity of the lines corresponds to the magnitude of x.\nFig. R9 Isoline of 𝒙= 𝑽𝟏+𝑽𝟐 .\n𝑽𝟏+𝑽𝟐+𝑽𝟑+𝑽𝟒\nSimilarly, we calculated and plotted the isolines for ratio y (represented by blue curves in Fig. R10).\nFinally, Fig. 3(g) was created by superimposing the isoline plots of x and y (Fig. R11).\nFig. R10 Isoline of 𝐲= 𝑽𝟏+𝑽𝟒 .\n𝑽𝟏+𝑽\n𝟐\n+𝑽𝟑+𝑽\nThe resulting figure demonstrates the distribution of these ratios across the plane. In the central region\n(light blue area), the isolines for both x and y are approximately parallel and equidistant, indicating a\nnearly linear relationship between the ratios and the spatial coordinates. In contrast, the isolines become\ndistorted in the peripheral regions (top and bottom of the X-axis for x, left and right of the Y-axis for y),\nindicating strong non-linearities. This figure effectively illustrates the inherent distortion that occurs\nwhen the ratios x and yare directly used as coordinates (x, y) in the traditional method, particularly outside\nthe central linear zone.\n2. Explanation of Fig. 3(h): Determining the Touch Point by Intersecting Isolines\nFigure 3(h) visually explains our method for determining the accurate touch coordinate by leveraging the\nisolines, thereby overcoming the distortion issue.\nThe plotting procedure was as follows:\nFirstly, when a touch occurs at a specific point on the interface, it generates four voltage pulses with\npeaks V , V , V , and V .\n1 2 3 4\nSecondly, from these voltages, we calculate the instantaneous ratios x and y using the formulas above.\n0 0\nThirdly, we then identify the specific isolines corresponding to the values x=x and y=y from the\n0 0\nprecomputed set of isolines covering the entire plane.\nFinally, these two isolines are plotted together (as shown in Fig. R12). The point where the x isoline and\n0\nthe y isoline intersect is the calculated touch point. This method accounts for the non-linear distribution\n0\nof the ratios, providing anti-distortion capability.\nFig. R12 Intersect of the x isoline and the y isoline. (Original Fig. 3(h))\n0 0\n3. Explanation of Fig. 3(k): Reconstruction of Multi-Finger Sliding Trajectories\nFigure 3(k) showcases the reconstructed sliding trajectories of two fingers, demonstrating the practical\napplication of our method.\nThe plotting procedure was as follows:\nFirstly, as two fingers slide across the BioAI2 interface, they generate a series of dense voltage pulse\npeaks on the four electrodes (Fig. R13(i)).\nSecondly, the raw data is acquired and then processed with a bandpass filter (50 Hz ~ 600 Hz) to remove\nlow-frequency signal components and baseline drift (Fig. R13(ii)).\nThirdly, valid pulse peaks that meet specific criteria are identified from the filtered signal (Fig. R13(iii)).\nFourthly, for each pair of valid peaks (corresponding to a specific time point), the touch coordinate is\ncalculated using the isoline intersection method described for Fig. 3(h) (Fig. R13(iv)).\nFinally, all the calculated coordinate points for each finger are plotted sequentially. The color of the points\ntransitions from red (early in time) to blue (later in time), forming the continuous trajectories shown in\nFig. 3(k).\nWe hope this detailed explanation clarifies the methodology behind these figures. We will incorporate\nthese descriptions into the revised manuscript to improve clarity. Thank you for this valuable feedback.\nFig. R13 Multi-finger stroking trajectory positioning ability. (i) First, collect the sliding data; (ii)\nSecond, in order to avoid the interference of the low-frequency signal, the bandpass filter is carried out,\nand then the signal pulse peak is found; (iii) Third, after finding suitable peaks on four signals, the\nratios in Equation (3) and (4) are then calculated; (iv) In the fourth step, the stroking trajectory points\non the two fingers are obtained based on the ratios and the isoline theory. According to the result,\nattributed to the super-high temporal resolution of the spike-like signals, the trajectory points from two\nfingers will not influence each other. (Fig.3 (k) is obtained here)\nMeanwhile, the corresponding modification has been highlighted in our revised manuscript. Specifically,\nthe details are shown below and Supplementary Note 4:\nFrom page 11, lines 14 to page 13, lines 9: “However, one bottleneck for the practical applications of\nthese methods is their low positioning accuracy due to the severe distortion deriving from approximate\ncoordinate calculation formulas37:\n𝑉 +𝑉\n𝑥 = 1 2 (3)\n𝑉 +𝑉 +𝑉 +𝑉\n1 2 3 4\n𝑉 +𝑉\n𝑦= 1 4 (4)\n𝑉 +𝑉 +𝑉 +𝑉\n1 2 3 4\nHerein, we have established an electrical theoretical model with the aim of providing a more refined\nmethod for position calculation. As stated in Supplementary Note 3, the stated formulas can be calculated\nas:\n1 1\n+\n𝑉 +𝑉 𝑅 𝑅\n𝑥 = 1 2 ≈ 1 2 (5)\n𝑉 +𝑉 +𝑉 +𝑉 1 1 1 1\n1 2 3 4 + + +\n𝑅 𝑅 𝑅 𝑅\n1 2 3 4\n1 1\n+\n𝑉 +𝑉 𝑅 𝑅\n𝑦= 1 4 ≈ 1 4 (6)\n𝑉 +𝑉 +𝑉 +𝑉 1 1 1 1\n1 2 3 4 + + +\n𝑅 𝑅 𝑅 𝑅\n1 2 3 4\nThus, we need to theoretically calculate the resistances between the touch point and four electrodes (R ,\n2 3 4\nmicroscopic perspective, as a uniform conductive plane composed of interconnected nanorods. In our\ncase, the film’s dimensions (10 cm × 10 cm) are significantly larger than the inter-electrode spacing,\nallowing us to approximate number of these connection is infinity. Therefore, it can be approximated as\na rectangular grid formed by connecting an infinite number of identical resistors r. Although this grid\nhas physical boundaries, the number of resistors within it approaches infinity, as illustrated in the\nSupplementary Figure 8. Consequently, the problem is transformed into calculating the resistance\nbetween any arbitrary point (m, n) on the plane and the electrode located at (0,0), where the number of\nresistors along both the horizontal and vertical dimensions of the grid is effectively infinite.\nConsider a rectangular network of resistors connected in an array of M × N nodes forming a network\nwith free boundaries as shown in Supplementary Figure 9. Number the nodes by coordinates (m, n), 0 ≤\nm ≤ M − 1, 0 ≤ n ≤ N − 1 and denote the resistances along the two principal directions by r and s.\nAccording the two-point resistance from Wu48, the resistance R between two nodes r = (x , y ) and r\nfree 1 1 1 2\n= (x , y ) is:\n2 2\n2\n𝑀−1 𝑁−1 |𝜓free −𝜓free |\n𝑅free (𝐫 ,𝐫 )= ∑ ∑ (𝑚,𝑛);(𝑥1,𝑦1) (𝑚,𝑛);(𝑥2,𝑦2)\n{𝑀×𝑁} 1 2 𝜆\n(𝑚,𝑛)\n𝑚=0 𝑛=0(𝑚,𝑛)≠(0,0)\n𝑟 𝑠 2\n= |𝑥 −𝑥 |+ |𝑦 −𝑦 |+ (7)\n𝑁 1 2 𝑀 1 2 𝑀𝑁\n1 1 1 1 2\n𝑀−1 𝑁−1 [𝑐𝑜s(𝑥 + )𝜃 𝑐𝑜s(𝑦 + )𝜙 −𝑐𝑜s(𝑥 + )𝜃 cos(𝑦 + )𝜙 ]\n1 2 𝑚 1 2 𝑛 2 2 𝑚 2 2 𝑛\n× ∑ ∑\n𝑟−1(1−𝑐𝑜s𝜃 )+𝑠−1(1−𝑐𝑜s𝜙 )\n𝑚 𝑛\n𝑚=1 𝑛=1\nwhere 𝜓free is eigenvector, 𝜆 is eigenvalue, 𝜃 = 𝑚𝜋 ,𝜙 = 𝑛𝜋 .\n(𝑚,𝑛);(𝑥1,𝑦1) (𝑚,𝑛) 𝑚 𝑀 𝑛 𝑁\nAs illustrated in Supplementary Figure 8, the model is configured with the following parameters: r = s,\nr = (0,0) and r = (x, y). The resistance between the two nodes can thus be expressed as:\n1 2\n𝑟𝑥 𝑟𝑦 2r\n𝑅free (𝐫 ,𝐫 )= + + ×\n{𝑀×𝑁} 1 2 𝑀 𝑁 𝑀𝑁\n𝑀−1 𝑁−1 [𝑐𝑜s(𝑥+\n1\n)𝜃 𝑐𝑜s(𝑦+\n1\n)𝜙 −𝑐𝑜s(𝑥 +\n1\n)𝜃 cos(𝑦 +\n1\n)𝜙 ]\n2\n2 𝑚 2 𝑛 2 2 𝑚 2 2 𝑛\n∑ ∑ (8)\n2−𝑐𝑜𝑠𝜃 −𝑐𝑜𝑠𝜙\n𝑚 𝑛\n𝑚=1 𝑛=1\nGiven that the conductive plane is sufficiently large, M and N can be regarded as approaching infinity.\nUnder this condition, Equation (8) can be further simplified to:\n𝑅𝑓𝑟𝑒𝑒(𝑟 ,𝑟 ) 𝑥 𝑦\n1 2\nlim = + +\n𝑟 𝑀 𝑁\n𝑀,𝑁→∞\n1 1 𝜃 𝜑 2\n1 𝜋 𝜋{𝑐𝑜𝑠[(𝑥+ 2 )𝜃]𝑐𝑜𝑠[(𝑦+ 2 )𝜑]−𝑐𝑜𝑠 2 𝑐𝑜𝑠 2 }\n∫ ∫ 𝑑𝜃𝑑𝜑 (9)\n𝜋2 sin2𝜃+sin2𝜑\n0 0\nIt can be observed from the derivation that Equation (9) is applicable to conductive films with finite\nboundaries. This confirms that the model aligns well with the physical reality, and consequently, the\nlocalization method derived from this theoretical framework does not introduce significant edge effects.\nTherefore, the relationship between electrode 1 (R1) and the position of the touch point (x,y) can be\nplotted as shown in Supplementary Figure 10. Similarly, the resistance between the touch point and other\nelectrodes can be also acquired. nd their corresponding isolines can be drawn as shown in Supplementary\nFigure 11 and Fig. 3g (Details are elaborated in Supplementary Note 4), from which we can find that the\nx, y and corresponding ratio obey a nearly linear relationship in the central region while the linear\nrelationship fails in the edge region, defined as the near-linear and non-linear regions, respectively. This\nis the critical factor causing the distortion of the formulas.\nOne way to solve the distortion problem is to improve the calculation formulas. Here, three different\ncalculation formulas are calculated and their isolines are drawn in Supplementary Figure 12-13. The\nresults reveal that the formulas calculated by the reciprocal of voltage demonstrate the largest linear area\nand best anti-distortion effect (Supplementary Figure 13-14). Nevertheless, these formulas still cannot\navoid the problem of distortion. Hence, inspired by the isolines theory40, we used the isolines in Fig. 3g\nto quantify the relationship between the position and the ratio of voltages. Figure 3(h) visually explains\nour method for determining the accurate touch coordinate by leveraging the isolines: (1) When a touch\noccurs at a specific point on the interface, it generates four voltage pulses with peaks V , V , V , and V ;\n1 2 3 4\n(2) From these voltages, the instantaneous ratios x and y can be calculated using the Equation (5) and\n0 0\n(6); (3) Then the specific isolines corresponding to the values x=x and y=y are identified from the\n0 0\nprecomputed set of isolines covering the entire plane; (4) These two isolines are plotted together (as\nshown in Fig. 3(h)). The point where the x isoline and the y isoline intersect is the calculated touch\n0 0\npoint. This method accounts for the non-linear distribution of the ratios, providing anti-distortion\ncapability.”\nAnd on page 14, lines 16 to 26: “As shown in Supplementary Figure 15c(ii), The exceptionally brief\nduration (<2 ms) of these spike-like pulses, contrasting with the prolonged signals from traditional plane\ninterfaces, provides intrinsic temporal separation for decoupling simultaneous or near-simultaneous\ntouches. Its time-frequency diagram presents significant high-frequency components compared to that\nof the plane interface. Attributing to these components, the multiple touches can be fully reconstructed\nby comparing the ratios of peaks using the isoline theory. As depicted in Fig. 3m, the super-high temporal\nresolution of the structure enables effective distinction between signals (peak ① and peak ②)\ngenerated in a very short time. Attributing to the high temporal resolution, continuous and even the two-\nfinger sliding trajectory are effectively reconstructed (Fig. 3n, Supplementary Figure 18-19, further\ndetails are discussed in Supplementary Note 5)”\n3. In Figure 3(j), the voltage vs. time graph shows multiple spikes. How does the peak detection\nmechanism manage variations in peak amplitude caused by inconsistent pressure or finger surface area?\nResponse to reviewer:\nThank you for raising this important question regarding the handling of peak amplitude variations caused\nby inconsistent pressure or finger contact area. Our peak detection mechanism is specifically designed\nto be robust against these real-world variations through a two-step algorithmic approach that addresses\nboth dynamic baseline drift and low-amplitude noise.\nThe key challenge lies in the fact that the raw signal is a mixture of the high-frequency neuro-mimetic\npulses and a low-frequency component corresponding to the dynamic applied pressure. This low-\nfrequency signal causes the baseline voltage to shift. Consequently, a pulse with a high actual amplitude\noccurring on a high-pressure baseline might appear smaller than a pulse with lower amplitude on a lower\nbaseline, rendering a simple fixed amplitude threshold completely ineffective.\nFurthermore, low-amplitude noise can introduce spurious peaks, further complicating detection.\nCritically, if a simple amplitude-threshold method were used, any small parasitic peak superimposed on\nthe shoulder of a main pulse would be assigned a high amplitude value (as illustrated in Fig. R15a),\nleading to false detections.\nTo overcome these challenges, we implemented a more sophisticated two-step method:\n1. Dynamic baseline correction via envelope extraction: First, we extract the envelope of the raw\nsignal. This envelope effectively tracks and characterizes the low-frequency dynamic baseline caused by\npressure (Fig. R14). By subtracting this envelope from the original signal, we effectively \"flatten\" the\nbaseline, ensuring all pulses are referenced to a consistent zero level. This step corrects for the amplitude\ncomparison distortion caused by varying pressure.\n2. Prominence-based peak identification: Instead of relying on absolute amplitude, we use peak\nprominence as the primary detection criterion. The prominence of a peak measures its height relative to\nthe surrounding valleys, effectively quantifying how much it stands out from its immediate local\nbackground. This method is highly effective at distinguishing significant, true pulses from minor\nfluctuations or noise. For example, even if a small parasitic peak appears on a main pulse, the main pulse\nretains a much higher prominence value. This ensures our algorithm reliably identifies the significant\nneuro-mimetic spikes while ignoring insignificant perturbations, as shown in Fig. R15b.\nIn conclusion, our peak detection mechanism does not rely on fixed amplitude thresholds, which are\nindeed unsuitable for this application. Instead, it leverages a robust two-step algorithm that corrects for\nbaseline drift and identifies peaks based on their local significance. This approach ensures accurate and\nreliable pulse detection despite the inherent variations in pressure and finger surface area that occur\nduring real-world use.\nFig. R14 Illustration of the signal envelope.\nFig. R15 Comparison of the height and the prominence of peak.\n4. What test has been performed to check the homogeneity of conductive film of BioAI2 device?\nResponse to reviewer:\nThanks for your kindly reminder.\nTo address the reviewer’s question regarding the homogeneity testing of the conductive film in the\nBioAI2 device, we performed a multi-faceted investigation combining microscopic structural analysis,\nmacroscopic electrical property mapping, and functional performance evaluation.\nA. Microscopic structural uniformity via scanning electron microscope (SEM) and atomic force\nmicroscopy (AfM)\nTo assess homogeneity at the micro-scale, we performed SEM on our conductive film. Two random\nsamples (0.5 cm × 0.5 cm each) were taken from a large-area film (10 cm × 10 cm) prepared by\nelectrospinning. The SEM results, shown in Fig. R16b-e, clearly demonstrate that the electrospinning\nprocess successfully produced a dense and uniformly distributed nanomesh structure. This microscopic\nuniformity is a primary indicator of the film’s homogeneous nature. Furthermore, we measured the\nthickness profile of the nanomesh through AFM (Fig. R16f-g). The data showed a maximum height\nvariation of less than 1.5 µm, which is negligible for a film with a total thickness exceeding 100 µm,\nand an average roughness (Ra) of 150 nm. These results provide strong evidence of high structural uniformity\nat the microscopic level.\nFig. R16 Surface topography characterization of the fabricated conductive layer. a Photograph of\nthe conductive layer. b Scanning electron microscope image (SEM) of the conductive layer. c Enlarge\nview of the image in b. d SEM image of the conductive layer in another part of the membrane. e\nEnlarge view of the image in (d). (Scale bars: 1 cm (a), 5 μm (b), 500 nm (c), 5 μm (d), 500 nm (e).)\nThe thickness and distribution of fibers in (b,c) and (d,e) are similar which indicates that the fabricated\nconductive layer is relatively uniform. f-h Atomic force microscope image (AFM) of the conductive\nlayer. R = 150 nm. (Scale bars: 2 μm (f), 6 μm (g)). The AFM reveals that the difference in height of\na\nthe conductive layer is lower than 1.5 μm.\n2. Macroscopic electrical homogeneity via equipotential line analysis\nWe further evaluated the macroscopic electrical homogeneity by analyzing the distribution of isolines\nacross the film surface. For comparison, we generated isoline maps for both a film produced by the\ntraditional liquid-evaporation method (Fig. R17a) and our electrospun nanomesh film (Fig. R17b). The\nisolines for the traditional film exhibited significant jaggedness and irregularity, which is caused by local\ninconsistencies in sheet resistance. In contrast, the isolines for our electrospun film are remarkably\nsmooth and continuous, which is a direct and strong evidence of highly uniform surface potential and\nsheet resistance across the entire area.\nFig. R17 Illustration of the homogeneity of conductive film fabricated by volatilization of a liquid\nfilm and electrostatic spinning. a (i) Illustration of volatilization of a liquid film. (ii) The measured\nisoline distribution of the conductive layer fabricated by volatilization of a liquid film. b (i) Illustration\nof a conductive layer by electrostatic spinning. (ii) The measured isoline distribution of the conductive\nlayer fabricated by electrostatic spinning.\nThe inhomogeneity in the traditional film arises from the evaporation process. During evaporation,\nvariables such as humidity and airflow cause differential evaporation rates across the liquid surface. This\nleads to regional variations in temperature and concentration, resulting in surface tension discrepancies\nthat trigger the Marangoni effect. This effect causes uneven material flow and, combined with the coffee-\nring effect at the edges, results in significant thickness variation in the solidified film. Our electrospinning\nprocess, conducted under controlled, constant temperature and humidity conditions, employs a layer-by-\nlayer deposition strategy. This method effectively avoids the pitfalls of the traditional approach, leading\nto a multi-layered, dense nanomesh with excellent thickness uniformity.\n3. Functional validation via positioning error assessment\nFinally, the homogeneity of the conductive film was validated indirectly through its functional\nperformance. We calculated the positioning error based on the electrical signals obtained from the film.\nThe results, presented in Fig. R18, show an average positioning error of 1.61 mm and a maximum error\nof 3.60 mm. This high level of positioning accuracy is functionally dependent on the electrical\nhomogeneity of the conductive layer. The low error values provide further, practical confirmation that\nthe film’s properties are consistent across its surface.\nIn summary, our comprehensive evaluation—from microscopic AFM imaging and macroscopic isoline\nanalysis to functional error calculation—consistently demonstrates that the conductive film used in the\nBioAI2 device exhibits excellent homogeneity.\nFig. R18 Position error map derived from the proposed isoline method.\nMeanwhile, the corresponding modification has been highlighted in our revised manuscript. Specifically,\nthe details are shown below:\nOn page 13, lines 23 to 37: “To assess homogeneity of the fabricated film at the micro-scale, we\nperformed SEM on our conductive film. Two random samples (0.5 cm × 0.5 cm each) were taken from\na large-area film (10 cm × 10 cm) prepared by electrospinning. As shown in Fig. 3i and Supplementary\nFigure 16a-e, the SEM results clearly demonstrate that the electrospinning process successfully produced\na dense and uniformly distributed nanomesh structure. Furthermore, we measured the thickness profile\nof the nanomesh through AFM (Supplementary Figure 16f-g). The data showed a maximum height\nvariation of less than 1.5 µm, which is negligible for a film with a total thickness exceeding 100 µm,\nand an average roughness (Ra) of 150 nm. These results provide strong evidence of high structural uniformity\nat the microscopic level. Furthermore, the isoline of the nanomesh membrane, as depicted in\nSupplementary Figure 15b(ii), demonstrates much more continuous and stable characteristics compared\nto that obtained via the solution method (Supplementary Figure 15a(ii)), attesting to its exceptional\nuniformity. The result demonstrates that nanotextured interface engineering (nanomesh structure) can\nreconcile conflicting requirements of high uniformity and large-area fabrication. Meanwhile, the\nmeasured isoline of the electrostatic spinning PVC nanomesh layer is similar to the contour distribution\nin Fig. 3g and further verifies the theory. To investigate the performance of the algorithm, the spatial\ndistribution of position error from two methods is exhibited in Fig. 3k. The inference accuracy of the\nposition of the traditional ratio-based method (average error of 3.49 mm, max error of 11.64 mm)\ndramatically declines with farther from the center while that of isoline theory (average error of 1.61mm,\nmax error of 3.60 mm) is relatively constant. Meanwhile, the repeatability of the theory is also\ndemonstrated in Supplementary Figure 17. For further verification, the locating results for different\npatterns based on traditional ratio methods and the isoline theory are compared in Fig. 3l. The ratio-based\nformulas present obvious distortions due to the influence from the non-linear region while the isoline\ntheory ensures good pattern reconstruction.”\nReviewer #3: Comments to Author\nThe manuscript \"Biomimetic hairy affective-touch sensory AI interface\" proposes a biomimetic,\ntriboelectric-based tactile interface designed to recognise affective touch and enable emotionally adaptive\nhuman–machine interaction. The system integrates artificial hairy skin, neuromimetic pulse generation,\ndeep learning-based emotion classification, and LLM-driven semantic feedback. The concept is\nambitious, and the prototype demonstrates some degree of integration and engineering effort. However,\nthe scientific foundation and experimental rigour lack sufficient depth and require significant\nimprovement before the work can be considered for publication.\nResponse to reviewer:\nWe sincerely thank you for the thoughtful assessment of our manuscript and for acknowledging the\nambitious nature of our work on a biomimetic affective-touch interface. We greatly appreciate the\nconstructive feedback regarding the need to strengthen the scientific foundation and experimental rigor\nof the study. We have enhanced the theoretical background and scientific justification for our biomimetic\napproach and expanded the experimental section to include more rigorous validation procedures,\nadditional data analyses. We believe these revisions have significantly improved the depth and robustness\nof our work. We have detailed all changes point-by-point in the response letter and highlighted them\nwithin the revised manuscript. We are grateful for the opportunity to improve our paper and hope that\nthe modifications adequately address the reviewer’s valid concerns.\nMore specifically,\n1. Page 6-7 “Neuromimetic stroke sensing of the BioAI2 enabled by triboelectricity” I cannot find a solid\ntheoretical model for the triboelectric pulse generation mechanism. While there is an explanation of spike\ngeneration (Fig. 2c–e), no modelling of surface potential dynamics or charge saturation is provided.\nThese are key factors, such as charge saturation, surface potential dynamics, along with material-\ndependent variability, which would need to be better modelled and validated.\nFig. R19 Theoretical model of BioAI2.\nResponse to reviewer:\nWe sincerely thank the reviewer for this insightful comment regarding the theoretical modeling of the\ntriboelectric pulse generation process in the BioAI2’s neuromimetic sensing interface. We agree that a solid\ntheoretical foundation is crucial. In response, we have developed a theoretical model focusing on the key\nfactors mentioned by the reviewer, including surface charge dynamics and the capacitive coupling in a\nsingle-hair contact scenario. This model provides a quantitative description of the spike generation\nmechanism.\nFor analytical simplicity, given the complexity of the contact interface involving multiple hairs, our primary\nanalysis centers on a simplified model of a single hair interacting with a finger. The contact-electrification\nprocess for a single hair in the BioAI2 is illustrated in Fig. R19a. The system involves multiple capacitors\nbetween the finger and the electrode, primarily comprising the contact capacitance at the touch point (C ),\nCon\nthe non-contact air gap capacitance (C ), and the dielectric capacitance of the hair (C ).\nAir D\nThe equivalent electrical model is simplified as shown in Fig. R19b. Dielectric 1 represents the skin, and\ndielectric 2 represents the hair’s media layer, with surface charge densities of −σ and +σ respectively,\ndue to contact electrification. The sensing electrode carries an induced charge Q, while the ground electrode has\na transferred charge −Q.\nUnder open-circuit (OC) conditions (Q=0), an equivalent circuit model was established (Fig. R19c). The\nkey nodes are: Node 1 (dielectric 1 surface), Node 2 (dielectric 2 surface), Node 3 (sensing electrode), and\nNode 4 (ground electrode). Capacitances exist between each node pair due to electric field lines. The total\ncapacitance between Node 3 and 4, C, is given by:\n0\n𝐶 𝐶 𝐶\nC =C + 𝐻 𝐷 𝐸 (𝑅4)\n𝐻 𝐷 𝐻 𝐸 𝐷 𝐸\nwhere C =C +C .\nH Air Con\nBased on charge conservation at each node and the defined potential relationships, we derived the following\nequation (the positive direction is shown in Fig. R19c):\nV =𝑉 +𝑉 +𝑉 (𝑅5)\nE 𝐻 𝐷 𝑆\nC 𝑉 +𝐶 𝑉 =𝜎𝑆 (𝑅6)\nE 𝐸 𝐻 𝐻\nC 𝑉 −𝐶 𝑉 =−𝜎𝑆 (𝑅7)\nD 𝐷 𝐻 𝐻\nC 𝑉 =𝐶 𝑉 (𝑅8)\nS 𝑆 𝐷 𝐷\n−C 𝑉 −𝐶 𝑉 =0 (𝑅9)\nE 𝐸 𝑆 𝑆\nCombine the Equation (R5-R9), we can derive the open-circuit voltage:\n𝐶 𝐶 𝜎𝑆\nV =𝑉 = 𝐸 𝐷 (𝑅10)\nS 𝑂𝐶 𝐶 𝐶 𝐶 +𝐶 𝐶 𝐶 +𝐶 𝐶 𝐶 +𝐶 𝐶 𝐶\n𝐻 𝐷 𝐸 𝐻 𝐷 𝑆 𝐻 𝐸 𝑆 𝐷 𝐸 𝑆\nFollowing the theoretical framework for TENGs23, the general voltage-charge relationship is:\nC 𝑂𝐶\nUnder short-circuit (SC) conditions (V=0), the short-circuit transferred charge (Q ) is:\nSC\n1\n0=− ×Q +𝑉 (𝑅12)\nC SC 𝑂𝐶\nQ =𝑉 𝐶 (𝑅13)\nSC 𝑂𝐶\nSubstituting Equation (R4) and (R10) into (R13) yields the core model for Q :\nSC\n𝜎𝑆\nQ =𝑉 𝐶 = (𝑅14)\nSC 𝑂𝐶 0 1 1\n𝐶 ×( + )+1\n𝐻 𝐶 𝐶\n𝐷 𝐸\nSince C =C +C , this becomes:\nH Air Con\n𝜎𝑆\nQ =𝑉 𝐶 = (𝑅15)\nSC 𝑂𝐶 0 1 1\n(𝐶 +𝐶 )×( + )+1\n𝐴𝑖𝑟 𝐶𝑜𝑛 𝐶 𝐶\n𝐷 𝐸\nThis model explicitly incorporates the surface charge density (σ) resulting from contact electrification,\naddressing the surface potential dynamics mentioned by the reviewer. The dynamics are governed by the\ntime-dependent changes in the capacitances C and C during the finger’s approach and contact with\nAir Con\nthe hair. There are two phases during this process.\n1. Non-contact phase (C = 0): The air gap capacitance is:\nCon\n𝜀 𝜀 𝑆\n𝐶 = 0 𝑟0 (𝑅16)\n𝐴𝑖𝑟 𝑑(𝑡)\nwhere ε is the dielectric constant of vacuum, ε is the relative dielectric constant of air, and d is the\n0 r0\ndistance between the finger and the hair.\nSubstituting Equation (R16) into Equation (R15), we have:\n𝜎𝑆\nQ = (𝑅17)\nSC 𝜀 𝜀 𝑆 1 1\n0 𝑟0 ×( + )+1\n𝑑(𝑡) 𝐶 𝐶\n𝐷 𝐸\nAccording to Equation (R16) and (R17), as the finger approaches (d(t) decreases), C increases,\nAir\nleading to a specific dynamic change in Q , which we calculated and plotted (see simulation in Fig.\nSC\nR20).\n2. Contact phase (C ≪ C ): Upon contact, the C is much smaller than the contact capacitance\nAir Con Air\nC . Therefore, the C can be neglected. The C can be calculated as:\nCon Air Con\n𝜀 𝜀 𝑆 (𝑡)\n𝐶 = 0 𝑟 1 (𝑅18)\n𝐶𝑜𝑛 𝑑\n1\nwhere the ε is the composite relative permittivity of the skin and dielectric layer 1, the contact area\nr\nS is a function of time, and d is the thickness of the contact capacitance, which can be regarded as\n1 1\na fixed value.\nSubstituting Equation (R18) into Equation (R15), we have:\n𝜎𝑆\nQ = (𝑅19)\nSC 𝜀 𝜀 𝑆 (𝑡) 1 1\n0 𝑟 1 ×( + )+1\n𝑑 𝐶 𝐶\n1 𝐷 𝐸\nC is a function of the contact area S (t), which changes over time during the sliding motion. This\nCon 1\nvariation directly drives the major change in Q .\nSC\nOn the basis of the discussion and the parameter as shown in Table R4, we can obtain the dynamic\nvariation of the transfer charge (Fig. R20).\nTable R4 Parameter used in simulation.\nDielectric ε =1, ε = 3, ε = 8.854*10-12, d = 100 μm\n0 r 0 1\nTribo-charge surface density 𝜎 = 8 μC m−2\nSize of electrode S = 10 mm2\nCapacitor 𝐶 =200 pF, C = 10 pF\n𝐷 E\nContact area S(t), calculated by simulation\nNon-contact Contact Non-contact\n1.5\n)\n2\nm\n1.0\nm\n(\n1 0.5\nS\n0.0\n0.0\nC)\nn\n(\n c-0.5\ns\nQ\n0 5 10\nTime (ms)\nFig. R20 Simulation result of contact area and the calculated short-circuit transfer charge.\nThe corresponding short-circuit current (I ) can be calculated as:\nSC\n𝑑𝑄\nI = 𝑠𝑐 (R20)\nsc 𝑑𝑡\nTherefore, the dynamic variation of I is shown in Fig. R21.\nSC\n0.4\n0.4\nQ\n( n\nC)\nsc\n0.0 0.0\nI\n(\n a.\nu.)\nsc\n-0.4\n-0.4\n0 2 4 6 8 10 12 14\nTime (ms)\nFig. R21 Simulated I from Q .\nSC SC\nThe results clearly demonstrate that the current signal exhibits a distinct neural spike-like behavior,\nstemming directly from the dynamics of the capacitive coupling modeled above.\nThe voltage output behavior under limited load (1 MΩ) is similar to the output behavior of I . And thus,\nSC\nthe neuromimetic pulse output is demonstrated.\nIn conclusion, this model provides a solid theoretical foundation for the pulse generation mechanism in\nBioAI2. It quantitatively links the surface charge (σ) to the output signal through the dynamics of the\ninterface capacitances, offering a direct response to the reviewer’s valuable points. We will add this\ndetailed derivation and discussion to the revised manuscript to enhance the theoretical depth of our work.\nMeanwhile, the corresponding modification has been highlighted in our revised manuscript. Specifically,\nthe details are shown below and Supplementary Note 1:\nOn page 7 lines 32 to 33: “Furthermore, a theoretical model of BioAI2 is established and the triboelectric\npulse generation mechanism are elaborated in Supplementary Note 1 and Supplementary Figure 2-4.”\nFurthermore, the analogy to C-LTMRs is not sufficiently substantiated (Abstract: “...inverted U-shaped\nfrequency-stroking speed response... resembles behavior of C-LTMRs”). While a visual comparison is\nmade (Fig. 2g–h), no physiological or biophysical model or neural encoding principles are provided to\nsupport the claimed biomimetic equivalence.\nWe thank the reviewer for this critical comment regarding the substantiation of the C-LTMR analogy.\nWe agree that a deeper physiological and biophysical foundation is essential to support the claimed\nbiomimetic equivalence. In our revised manuscript, we have significantly expanded our discussion to\nprovide a more rigorous justification, which we summarize below based on three key aspects:\nstructural\nanalogy, neuronal encoding principles, and functional response characteristics.\nFig. R22 Analogy of BioAI2 to C-LTMRs. a C-LTMR generates spikes due to hair deflection24. b\nBioAI2 generates spikes due to hair deflection. c Ion-gated channels within organisms enable\ninformation transmission25. d Leaky IF neuron model. When the potential exceeds a threshold θ, a\nspike is generated25. e Leaky IF neuron model of BioAI2.\n1. Structural analogy: hairy skin as a shared sensory platform\nBoth the BioAI2 interface and biological C-LTMRs operate within the context of hairy skin. This is a\nfundamental architectural similarity. In mammals, C-LTMRs specifically innervate hairy skin, where hair\nfollicles act as intrinsic mechanical levers that amplify gentle tactile stimuli24,26 (Fig. R22a). Similarly,\nthe BioAI2’s design features an array of artificial hairs (Fig. R22b). These hairs serve as the primary\nmechanical interface that transduces mechano-stimulation into electrical signals, mimicking the role of\nhair follicles in directing mechanical forces onto the C-LTMR endings in biological systems. This\nestablishes a foundational structural biomimesis where the external stimulus pathway is analogous.\n2. Neuro-mimetic encoding principles: a coupled biophysical model\nTo move beyond a mere visual comparison, we have developed a coupled biophysical model that links\nthe device’s physics to neural encoding principles. As detailed in the previous question, the triboelectric\neffect governs the charge generation (V, Q) in response to mechanical contact and separation (simulating\ntouch). This electrical signal serves as the input to a simplified leaky integrate-and-fire (LIF) neuron\nmodel. The LIF model is a well-established computational framework in neuroscience for simulating the\nmembrane potential dynamics of neurons (Fig. R22c,d). In our model, the triboelectrically induced\ncurrent charges a capacitor representing the neuronal membrane (Fig. R22e). Once the hand contacts and\nleaves the hair, the capacitor will be charged and released, and a spike is generated. The hair acts as a\nmechanical switch here. This coupling directly simulates the process of mechano-transduction and\nencoding in a primary afferent: mechanical stimulus → receptor potential → action potential generation.\nThis approach provides a principled model, rather than a mere analogy, for the spike generation\nmechanism that underlies the neuromimetic behavior.\n3. Functional biomimesis: quantifying the “inverted U-shaped” response\nWe have further strengthened the analogy by quantitatively comparing the stimulus-response functions.\nA hallmark of C-LTMR physiology is their inverted U-shaped response to stroking speed, with peak\nsensitivity in the slow range (approximately 1-10 cm/s in humans, correlating with pleasant touch27). Our\nexperimental results and model simulations demonstrate that BioAI2 exhibits a strikingly similar inverted\nU-shaped frequency-stroking speed response. The following table provides a direct, quantitative\ncomparison of key response properties between BioAI2 and biological C-LTMRs, substantiating the\nfunctional equivalence beyond visual similarity.\nTable R5 Comparison between BioAI2 and biological C-LTMRs\nBiological C-\nResponse BioAI2\nLTMRs Biomimetic Significance\nCharacteristic Performance\n(Literature)\nOptimal Prefer slow speeds Both are tuned to velocities associated\n30 - 80 mm/s\nStroking Speed (e.g., 1-10 cm/s) with affective, non-discriminative touch.\nEffective Speed Both operate within a defined dynamic\n0 - 300 mm/s 0 - 300 mm/s\nRange range of gentle mechanical stimuli.\nThe non-monotonic response is a key\nResponse Curve Inverted U-\nInverted U-shaped functional signature of C-LTMRs, which\nShape shaped\nBioAI2 successfully replicates.\nFurthermore, the BioAI2’s response curve can be modulated by structural parameters (e.g., hair height,\nstiffness, spacing), which alter the capacitive coupling and charge transfer dynamics (as described by\nEquation (R16-R20)). This tunability mirrors the biological variability and adaptation found in sensory\nsystems and allows us to engineer the device’s specific response profile to closely match that of C-\nLTMRs.\nIn conclusion, the analogy between BioAI2 and C-LTMRs is supported by a multi-level correspondence:\n(1) a shared structural basis in hairy skin mechanics, (2) a biophysically-grounded model that translates\nmechanical stimulation into neuromimetic spikes based on neural encoding principles, and (3) a\nquantitative replication of the defining inverted U-shaped response function to stroking speed. We believe\nthis comprehensive rationale solidifies the claimed biomimetic equivalence. We thank the reviewer for\nprompting this important clarification and have incorporated these points into the revised manuscript.\nMeanwhile, the corresponding modification has been highlighted in our revised manuscript. Specifically,\nthe details are shown below and Supplementary Note 2:\nOn page 8, lines 3 to 26: “In biological systems, C-LTMRs precisely employ such nonlinear encoding to\ndistinguish between affective caresses and non-emotional mechanical stimuli. The reproduction of this\ncharacteristic neuromorphic coding pattern enables the device to mimic the neural basis of emotional\nvalence processing in the insular cortex22,23. This consistency is not superficial but is grounded in a multi-\nlevel biomimetic analogy. First, from a structural perspective, both the BioAI2 interface and biological\nC-LTMRs operate within the context of hairy skin, where artificial hairs or natural hair follicles serve as\nintrinsic mechanical levers that amplify gentle tactile stimuli. Second, the encoding principle is\nneuromimetic: we have developed a coupled biophysical model where the triboelectric effect (governing\ncharge generation in response to mechanical contact-separation) drives a leaky integrate-and-fire (LIF)\nneuron model (Supplementary Note 2, Supplementary Figure 5). This simulates the mechanotransduction\nand encoding process in primary afferents, from mechanical stimulus to receptor potential and ultimately\nto action potential generation, thus providing a principled foundation for the spike generation mechanism\nthat goes beyond a mere visual analogy.\nThrough reasonable adjustment of the hairy structure, the signal output can well approximate the\nbehavior of biological neural signals. It is found that the output frequency of the interface presents the\nhighest frequency between 10 mm/s and 200 mm/s when the gap reaches 3 mm. This consistency with\nC-LTMRs provides a quantifiable standard for evaluating emotional states by observing the\ncharacteristics of the signal (pulse frequency) which has not been achieved before. In addition, the\noutputs of different d with the gap of 3 mm are also explored in Fig. 2h and the result with the hair width\nof 2 mm presents the best fit. Thus, the device structure in subsequent experiments adopts this\nconfiguration. This quantitative replication confirms that BioAI2 effectively captures the key nonlinear\nencoding strategy used by C-LTMRs to distinguish affective caresses from non-emotional mechanical\nstimuli.”\nIt is recommended that authors consider including some additional theoretical and experimental\nvalidation of the triboelectric mechanism and neuro-mimetic claims, including some quantitative data of\nmaterial physics (i.e., charge density dynamics, triboelectric charge decay, energy band alignment).\nLine 109, avoid using overstatements such as “With its exceptional lightness and flexibility,…” when no\ndata supports these types of statements.\nWe sincerely thank the reviewer for the constructive suggestions.\nWe have established a solid theoretical framework to validate the triboelectric mechanism as stated in\nprevious questions.\nPlot Voltage\na 9.14328 ± 0.05995\nb -1079.88895 ± 7.4782\n2 2 Reduced Chi-Sqr 0.00103\nR-Square (COD) 0.99584\nV) Adj. R-Square 0.99583\ne\n( 1\ng\na\nolt1\nV 0\n0.007 0.008\n0\n0.00 0.01 0.02\nTime (s)\nFig. R23 Exponential fitting of pulsed output of BioAI2.\nFig. R24 a Modulating neural stimulation by optimizing the waveform output of the TENG29. b Nerve\nstimulation using the exponential output of TENG31.\nRegarding the neuromimetic nature of the TENG’s output, we have expanded our discussion to firmly\nground our claims in the existing body of literature. The pulsed output of TENGs shares key\ncharacteristics with neural action potentials, such as a sharp rising phase and an exponential decay (as\nshown in Fig. R23), making them inherently suitable for neurostimulation. Numerous studies have\nsuccessfully utilized TENGs to generate neuromorphic pulses. The output characteristics of these pulses,\nwhich resemble neural signals, have enabled their application in stimulating various neural tissues,\nincluding the bladder pelvic nerve28, rat sensory cortex29, sciatic nerves and common peroneal nerves30,\nfor purposes such as muscle stimulation and neural signal transmission. For instance, Wang et al. 29\nachieved effective neural stimulation by optimizing the waveform output of the TENG (Fig. R24a).\nFurthermore, research by Lee31 demonstrated that the exponential waveforms naturally generated by\nTENGs exhibit higher stimulation efficiency compared to conventional square pulsed waveforms\ntypically used for nerve stimulation (Fig. R24b).\nThese findings collectively indicate that the pulsed output of TENGs can effectively mimic or substitute\nfor natural neural pulses, underscoring the significant functional similarity between TENG outputs and\nauthentic neural signals\nFig. R25 Optical images of the BioAI2. a Appearance of the BioAI2. b-d Flexibility of the BioAI2.\nRegarding the overstatements, we thank the reviewer for pointing this out. We have replaced them with\nquantitative data and demonstration to objectively characterize the device’s properties. Specifically, the\nweight of the interface has been measured and the flexibility of BioAI2 can be demonstrated in Fig. R25.\nMeanwhile, the corresponding modification has been highlighted in our revised manuscript. Specifically,\nthe details are shown below and Supplementary Figure 1:\nOn page 3, lines 21 to 23: “Owing to its lightness (0.25 g/cm2) and flexibility (Supplementary Figure 1),\nthe BioAI2 can be assembled on robots effortlessly, enabling real-time fusion of multi-dimensional tactile\ndata (force, velocity, contact topology).”\n2. The assertion that pulse frequencies convey emotional information is not backed by formal\ninformation-theoretic metrics. Without mutual information analysis, entropy estimation, or signal\nseparability validation, the claim of “emotion encoding” remains speculative.\nIt is recommended that the authors provide better justification of the corresponding claims and more\nevidence than reported at page 6, Fig.2f and 2g caption, page 15 lines 502-503, page 18-19 lines 599-\n600 “...the spike frequency is positively correlated with the stroking velocity...”, and Fig.4c “Box plots\nof the spike frequency and exerted force under different gestures and emotions”.\nResponse to reviewer:\nWe sincerely thank the reviewer for raising this critical point regarding the need for formal information-\ntheoretic metrics to substantiate our claim that pulse frequencies convey emotional information. We agree\nthat robust quantitative validation is essential. In response, we have conducted additional statistical\nanalyses that serve as rigorous, distribution-free measures of signal separability and encoding validity,\naddressing the spirit of the reviewer’s comment.\nOur justification is based on two primary statistical approaches that validate the discriminative power of\npulse signal features (including frequency) for emotion recognition:\n1. Non-parametric hypothesis testing for signal separability: Since our physiological data (pulse\nfrequency, force, etc.) did not follow a normal distribution (as confirmed by normality tests), we\nemployed the Kruskal-Wallis test, a non-parametric method equivalent to a one-way ANOVA for non-\nnormal data. This test assesses whether samples originate from the same distribution. The results,\nsummarized in Table R6, are conclusive:\nFor pulse frequency, the test statistics against both action type (H = 10885.923, p < 0.001) and emotion\ntype (H = 1219.658, p < 0.001) are highly significant. This confirms that the pulse frequency feature is\nnot random but is systematically modulated by both the performed action and the underlying emotion,\nproviding a statistical foundation for its role in encoding.\nTable R6 Kruskal-Wallis Test\nAction Emotion\nParameter\nH p H p\nForce 18172.467 0.000 186.550 0.000\nFrequency 10885.923 0.000 1219.658 0.000\nTime 10800.832 0.000 2145.415 0.000\nStd 18148.679 0.000 2789.719 0.000\nMean 220.571 0.641 0.890 0.641\n2. Multivariate analysis of variance (MANOVA) for feature validation: To further validate the\ncollective and individual contribution of multiple signal features (frequency, force, time, standard\ndeviation, mean voltage) in distinguishing emotions and actions, we performed a MANOVA. The\nMANOVA results (Table R7) robustly support our claims:\nPulse frequency again shows a highly significant effect on both action (F = 4964.098, p < 0.01) and\nemotion (F = 25.427, p < 0.01). The significant effect on emotion, albeit with a smaller F-value than for\naction, confirms its specific role in emotional encoding beyond mere motion artifact.\nCrucially, the analysis also acts as an internal control: Mean voltage showed no significant effect on\nemotion classification (F = 2.239, p = 0.107), demonstrating that our method can distinguish which\nfeatures are informative and which are not. This strengthens the specific claim for pulse frequency.\nTable R7 MANOVA result.\nAction Emotion\nParameter\nF p Partial η2 F p Partial η2\nForce 6085.893 0.000 0.613 1023.174 0.000 0.071\nFrequency 4964.098 0.000 0.270 25.427 0.000 0.013\nTime 2145.096 0.000 0.527 1865.835 0.000 0.065\nStd 39515.320 0.000 0.595 491.108 0.000 0.113\nMean 14.755 0.000 0.004 2.239 0.107 0.000\nIn conclusion, the Kruskal-Wallis test and MANOVA provide powerful, distribution-free statistical\nevidence for the encoding of emotional information in pulse frequencies. The extremely low p-values (p\n< 0.01) demonstrate that the observed differences in pulse frequency across emotions are highly unlikely\nto occur by chance, thereby validating the signal’s separability and its role in encoding. Additionally, we\npresent a violin plot (Fig. R26) depicting the distribution of frequencies across different emotional states\nfor stroke. The plot shows a significant correlation with the median frequency, providing additional\nevidence for the hypothesis that pulse frequencies convey emotional information.\n25%~75%\nz)\nH\ny\n(\nc\ne n 50\nu\nq\ne\nFr\nEmotion\nFig. R26 Distribution of frequencies across different emotional states for stroke.\nMeanwhile, the corresponding modification has been highlighted in our revised manuscript. Specifically,\nthe details are shown below:\nOn page 7, lines 34 to 37: “The outputs of the BioAI2 in response to different stroking speeds are\npresented in Fig. 2e. To examine the relationship between the output spike frequency and stroking\nspeed, an experiment was conducted using hairs with standardized dimensions: long and short hairs had\nheights of 3 mm and 1.5 mm, respectively, and a constant width (d) of 2 mm.”\nAnd on page 19, lines 16 to 29: “To statistically validate the separability and significance of these\nobserved signal features, we conduct two types of hypothesis tests, 1) Kruskal-Wallis test and 2)\nmultivariate analysis of variance (MANOVA) with action type and emotion type as dependent variables.\nThe Kruskal-Wallis test results demonstrate highly significant effects for both action type and emotion\ntype across key parameters. For pulse frequency, the test statistics were H = 10885.923 (p < 0.001) against\naction and H = 1219.658 (p < 0.001) against emotion. Similarly, force (H = 18172.467, p < 0.001), time\n(H = 10800.832, p < 0.001), and standard deviation (H = 18148.679, p < 0.001) showed significant\nseparability, while mean voltage did not (H = 220.571, p = 0.641). This confirms that features like pulse\nfrequency are systematically modulated by actions and emotions, providing a statistical foundation for\ntheir role in tactile encoding. The MANOVA results reinforced the Kruskal-Wallis findings: pulse\nfrequency exhibited a highly significant effect on both action (F = 4964.098, p < 0.01) and emotion (F =\n25.427, p < 0.01). Force, time, and standard deviation also showed significant effects, whereas mean\nvoltage had no significant impact on emotion classification (F = 2.239, p = 0.107).”\n3. Pages 9-11, Fig. 3g-i. The isoline-based localisation method is promising but mathematically\nunderdeveloped in the main text. Assumptions like uniform resistance and infinite boundaries are\nidealised and remain not fully validated against actual fabrication tolerances. Despite the supplementary\nnotes 1-2 and supplementary Figs. 3-8, the isoline derivation method assumes infinite uniform resistor\ngrid and lacks quantitative analysis of real-world fabrication variation. It is recommended that the authors\nprovide quantitative data on conductivity variation and edge effects.\nResponse to reviewer:\nWe sincerely thank the reviewer for raising these critical points regarding the mathematical foundation\nof our isoline-based localization method and the validation of its underlying assumptions. Below, we\nprovide a detailed clarification and present quantitative evidence to address these concerns.\n1. Clarification on the boundary condition assumption\nThe reviewer correctly highlighted the potential issue of assuming infinite boundaries. We would like to\nclarify that our theoretical model does not rely on an infinite boundary condition but is derived for a\nfinite rectangular conductive film.\nThe rectangular conductive film fabricated via electrospinning can be viewed, from a microscopic\nperspective, as a uniform conductive plane composed of interconnected nanorods. In our case, the film’s\ndimensions (10 cm×10 cm) are significantly larger than the inter-electrode spacing, allowing us to\napproximate number of these connection is infinity. Therefore, it can be approximated as a rectangular\ngrid formed by connecting an infinite number of identical resistors r. Although this grid has physical\nboundaries, the number of resistors within it approaches infinity, as illustrated in the figure below.\nFig. R27 Finite boundary rectangular conductive film.\nConsequently, the problem is transformed into calculating the resistance between any arbitrary point (m,\nn) on the plane and the electrode located at (0,0), where the number of resistors along both the horizontal\nand vertical dimensions of the grid is effectively infinite.\nConsider a rectangular network of resistors connected in an array of M × N nodes forming a network\nwith free boundaries as shown in Fig. R28.\nFig. R28 A M × N rectangular network.\nNumber the nodes by coordinates (m, n), 0 ≤ m ≤ M − 1, 0 ≤ n ≤ N − 1 and denote the resistances along\nthe two principal directions by r and s.\nAccording the two-point resistance from Wu21, the resistance R between two nodes r = (x , y ) and r\nfree 1 1 1 2\n= (x , y ) is:\n2 2\n2\n𝑀−1 𝑁−1 |𝜓free −𝜓free |\n𝑅free (𝐫 ,𝐫 )= ∑ ∑ (𝑚,𝑛);(𝑥1,𝑦1) (𝑚,𝑛);(𝑥2,𝑦2)\n{𝑀×𝑁} 1 2 𝜆\n(𝑚,𝑛)\n𝑚=0 𝑛=0(𝑚,𝑛)≠(0,0)\n𝑟 𝑠 2\n= |𝑥 −𝑥 |+ |𝑦 −𝑦 |+ (𝑅21)\n𝑁 1 2 𝑀 1 2 𝑀𝑁\n1 1 1 1 2\n𝑀−1 𝑁−1 [𝑐𝑜s(𝑥 + )𝜃 𝑐𝑜s(𝑦 + )𝜙 −𝑐𝑜s(𝑥 + )𝜃 cos(𝑦 + )𝜙 ]\n1 2 𝑚 1 2 𝑛 2 2 𝑚 2 2 𝑛\n× ∑ ∑\n𝑟−1(1−𝑐𝑜s𝜃 )+𝑠−1(1−𝑐𝑜s𝜙 )\n𝑚 𝑛\n𝑚=1 𝑛=1\nwhere 𝜓free is eigenvector, 𝜆 is eigenvalue, 𝜃 = 𝑚𝜋 ,𝜙 = 𝑛𝜋 .\n(𝑚,𝑛);(𝑥1,𝑦1) (𝑚,𝑛) 𝑚 𝑀 𝑛 𝑁\nAs illustrated in Fig. R27, the model is configured with the following parameters: r = s, r = (0,0) and r\n1 2\n= (x, y). The resistance between the two nodes can thus be expressed as:\n𝑟𝑥 𝑟𝑦 2r\n𝑅 free (𝐫 ,𝐫 )= + + ×\n{𝑀×𝑁} 1 2 𝑀 𝑁 𝑀𝑁\n1 1 1 1 2\n𝑀−1 𝑁−1\n[𝑐𝑜s(𝑥+ )𝜃 𝑐𝑜s(𝑦+ )𝜙 −𝑐𝑜s(𝑥 + )𝜃 cos(𝑦 + )𝜙 ]\n2 𝑚 2 𝑛 2 2 𝑚 2 2 𝑛\n∑ ∑ (R22)\n2−𝑐𝑜𝑠𝜃 −𝑐𝑜𝑠𝜙\n𝑚 𝑛\n𝑚=1 𝑛=1\nGiven that the conductive plane is sufficiently large, M and N can be regarded as approaching infinity.\nUnder this condition, Equation (R22) can be further simplified to:\n𝑅𝑓𝑟𝑒𝑒(𝑟 ,𝑟 ) 𝑥 𝑦\n1 2\nlim = + +\n𝑀,𝑁→∞ 𝑟 𝑀 𝑁\n1 1 𝜃 𝜑 2\n1 𝜋 𝜋{𝑐𝑜𝑠[(𝑥+ 2 )𝜃]𝑐𝑜𝑠[(𝑦+ 2 )𝜑]−𝑐𝑜𝑠 2 𝑐𝑜𝑠 2 }\n∫ ∫ 𝑑𝜃𝑑𝜑 (𝑅23)\n𝜋2 sin2𝜃+sin2𝜑\n0 0\nIt can be observed from the derivation that Equation (R23) is applicable to conductive films with finite\nboundaries. This confirms that the model aligns well with the physical reality, and consequently, the\nlocalization method derived from this theoretical framework does not introduce significant edge effects.\n2. Quantitative validation of conductivity uniformity and fabrication tolerance\nTo address the concern regarding resistance uniformity and fabrication variations, we conducted a multi-\nscale experimental analysis:\nA. Microscopic structural uniformity via scanning electron microscope (SEM) and atomic force\nmicroscopy (AfM)\nTo assess homogeneity at the micro-scale, we performed SEM on our conductive film. Two random\nsamples (0.5 cm × 0.5 cm each) were taken from a large-area film (10 cm × 10 cm) prepared by\nelectrospinning. The SEM results, shown in Fig. R29b-e, clearly demonstrate that the electrospinning\nprocess successfully produced a dense and uniformly distributed nanomesh structure. This microscopic\nuniformity is a primary indicator of the film’s homogeneous nature. Furthermore, we measured the\nthickness profile of the nanomesh through AFM (Fig. R29f-g). The data showed a maximum height\nvariation of less than 1.5 µm, which is negligible for a film with a total thickness exceeding 100 µm,\nand an average roughness (Ra) of 150 nm. These results provide strong evidence of high structural uniformity\nat the microscopic level.\nFig. R29 Surface topography characterization of the fabricated conductive layer. a Photograph of\nthe conductive layer. b Scanning electron microscope image (SEM) of the conductive layer. c Enlarge\nview of the image in b. d SEM image of the conductive layer in another part of the membrane. e\nEnlarge view of the image in d. (Scale bars: 1 cm (a), 5 μm (b), 500 nm (c), 5 μm (d), 500 nm (e).) The\nthickness and distribution of fibers in (b,c) and (d,e) are similar which indicates that the fabricated\nconductive layer is relatively uniform. f-h Atomic force microscope image (AFM) of the conductive\nlayer. R = 150 nm. (Scale bars: 2 μm (f), 6 μm (g)). The AFM reveals that the difference in height of\na\nthe conductive layer is lower than 1.5 μm.\nB. Macroscopic electrical homogeneity via equipotential line analysis\nWe further evaluated the macroscopic electrical homogeneity by analyzing the distribution of isolines\nacross the film surface. For comparison, we generated isoline maps for both a film produced by the\ntraditional liquid-evaporation method (Fig. R30a) and our electrospun nanomesh film (Fig. R30b). The\nisolines for the traditional film exhibited significant jaggedness and irregularity, which is caused by local\ninconsistencies in sheet resistance. In contrast, the isolines for our electrospun film are remarkably\nsmooth and continuous, which is a direct and strong evidence of highly uniform surface potential and\nsheet resistance across the entire area.\nThe inhomogeneity in the traditional film arises from the evaporation process. During evaporation,\nvariables such as humidity and airflow cause differential evaporation rates across the liquid surface. This\nleads to regional variations in temperature and concentration, resulting in surface tension discrepancies\nthat trigger the Marangoni effect. This effect causes uneven material flow and, combined with the coffee-\nring effect at the edges, results in significant thickness variation in the solidified film. Our electrospinning\nprocess, conducted under controlled, constant temperature and humidity conditions, employs a layer-by-\nlayer deposition strategy. This method effectively avoids the pitfalls of the traditional approach, leading\nto a multi-layered, dense nanomesh with excellent thickness uniformity.\nFig. R30 Illustration of the homogeneity of conductive film fabricated by volatilization of a liquid\nfilm and electrostatic spinning a (i) Illustration of volatilization of a liquid film. (ii) The measured\nisoline distribution of the conductive layer fabricated by volatilization of a liquid film. b (i) Illustration\nof a conductive layer by electrostatic spinning. (ii) The measured isoline distribution of the conductive\nlayer fabricated by electrostatic spinning.\nC. Functional validation via positioning error assessment\nFinally, the homogeneity of the conductive film was validated indirectly through its functional\nperformance. We calculated the positioning error based on the electrical signals obtained from the film.\nThe results, presented in Fig. R31, show an average positioning error of 1.61 mm and a maximum error\nof 3.60 mm. This high level of positioning accuracy is functionally dependent on the electrical\nhomogeneity of the conductive layer. The low error values provide further, practical confirmation that\nthe film’s properties are consistent across its surface.\nIn summary, our comprehensive evaluation—from microscopic AFM imaging and macroscopic isoline\nanalysis to functional error calculation—consistently demonstrates that the conductive film used in the\nBioAI2 device exhibits excellent homogeneity.\nFig. R31 Position error map derived from the proposed isoline method.\nMeanwhile, the corresponding modification has been highlighted in our revised manuscript. Specifically,\nthe details are shown below:\nFrom page 11, lines 14 to page 12, lines 14:“ However, one bottleneck for the practical applications of\nthese methods is their low positioning accuracy due to the severe distortion deriving from approximate\ncoordinate calculation formulas20:\n𝑉 +𝑉\n𝑥 = 1 2 (3)\n𝑉 +𝑉 +𝑉 +𝑉\n1 2 3 4\n𝑉 +𝑉\n𝑦= 1 4 (4)\n𝑉 +𝑉 +𝑉 +𝑉\n1 2 3 4\nHerein, we have established an electrical theoretical model with the aim of providing a more refined\nmethod for position calculation. As stated in Supplementary Note 3, the stated formulas can be calculated\nas:\n1 1\n+\n𝑉 +𝑉 𝑅 𝑅\n𝑥 = 1 2 ≈ 1 2 (5)\n𝑉 +𝑉 +𝑉 +𝑉 1 1 1 1\n1 2 3 4 + + +\n𝑅 𝑅 𝑅 𝑅\n1 2 3 4\n1 1\n+\n𝑉 +𝑉 𝑅 𝑅\n𝑦= 1 4 ≈ 1 4 (6)\n𝑉 +𝑉 +𝑉 +𝑉 1 1 1 1\n1 2 3 4 + + +\n𝑅 𝑅 𝑅 𝑅\n1 2 3 4\nThus, we need to theoretically calculate the resistances between the touch point and four electrodes (R ,\n2 3 4\nmicroscopic perspective, as a uniform conductive plane composed of interconnected nanorods. In our\ncase, the film’s dimensions (10 cm × 10 cm) are significantly larger than the inter-electrode spacing,\nallowing us to approximate number of these connection is infinity. Therefore, it can be approximated as\na rectangular grid formed by connecting an infinite number of identical resistors r. Although this grid\nhas physical boundaries, the number of resistors within it approaches infinity, as illustrated in the\nSupplementary Figure 8. Consequently, the problem is transformed into calculating the resistance\nbetween any arbitrary point (m, n) on the plane and the electrode located at (0,0), where the number of\nresistors along both the horizontal and vertical dimensions of the grid is effectively infinite.\nConsider a rectangular network of resistors connected in an array of M × N nodes forming a network\nwith free boundaries as shown in Supplementary Figure 9. Number the nodes by coordinates (m, n), 0 ≤\nm ≤ M − 1, 0 ≤ n ≤ N − 1 and denote the resistances along the two principal directions by r and s.\nAccording the two-point resistance from Wu46, the resistance R between two nodes r = (x , y ) and r\nfree 1 1 1 2\n= (x , y ) is:\n2 2\n2\n𝑀−1 𝑁−1 |𝜓free −𝜓free |\n𝑅free (𝐫 ,𝐫 )= ∑ ∑ (𝑚,𝑛);(𝑥1,𝑦1) (𝑚,𝑛);(𝑥2,𝑦2)\n{𝑀×𝑁} 1 2 𝜆\n(𝑚,𝑛)\n𝑚=0 𝑛=0(𝑚,𝑛)≠(0,0)\n𝑟 𝑠 2\n= |𝑥 −𝑥 |+ |𝑦 −𝑦 |+ (7)\n𝑁 1 2 𝑀 1 2 𝑀𝑁\n1 1 1 1 2\n𝑀−1 𝑁−1 [𝑐𝑜s(𝑥 + )𝜃 𝑐𝑜s(𝑦 + )𝜙 −𝑐𝑜s(𝑥 + )𝜃 cos(𝑦 + )𝜙 ]\n1 2 𝑚 1 2 𝑛 2 2 𝑚 2 2 𝑛\n× ∑ ∑\n𝑟−1(1−𝑐𝑜s𝜃 )+𝑠−1(1−𝑐𝑜s𝜙 )\n𝑚 𝑛\n𝑚=1 𝑛=1\nwhere 𝜓free is eigenvector, 𝜆 is eigenvalue, 𝜃 = 𝑚𝜋 ,𝜙 = 𝑛𝜋 .\n(𝑚,𝑛);(𝑥1,𝑦1) (𝑚,𝑛) 𝑚 𝑀 𝑛 𝑁\nAs illustrated in Supplementary Figure 8, the model is configured with the following parameters: r = s,\nr = (0,0) and r = (x, y). The resistance between the two nodes can thus be expressed as:\n1 2\n𝑟𝑥 𝑟𝑦 2r\n𝑅free (𝐫 ,𝐫 )= + + ×\n{𝑀×𝑁} 1 2 𝑀 𝑁 𝑀𝑁\n𝑀−1 𝑁−1 [𝑐𝑜s(𝑥+\n1\n)𝜃 𝑐𝑜s(𝑦+\n1\n)𝜙 −𝑐𝑜s(𝑥 +\n1\n)𝜃 cos(𝑦 +\n1\n)𝜙 ]\n2\n2 𝑚 2 𝑛 2 2 𝑚 2 2 𝑛\n∑ ∑ (8)\n2−𝑐𝑜𝑠𝜃 −𝑐𝑜𝑠𝜙\n𝑚 𝑛\n𝑚=1 𝑛=1\nGiven that the conductive plane is sufficiently large, M and N can be regarded as approaching infinity.\nUnder this condition, Equation (8) can be further simplified to:\n𝑅𝑓𝑟𝑒𝑒(𝑟 ,𝑟 ) 𝑥 𝑦\n1 2\nlim = + +\n𝑀,𝑁→∞ 𝑟 𝑀 𝑁\n1 1 𝜃 𝜑 2\n1 𝜋 𝜋{𝑐𝑜𝑠[(𝑥+ 2 )𝜃]𝑐𝑜𝑠[(𝑦+ 2 )𝜑]−𝑐𝑜𝑠 2 𝑐𝑜𝑠 2 }\n∫ ∫ 𝑑𝜃𝑑𝜑 (9)\n𝜋2 sin2𝜃+sin2𝜑\n0 0\nIt can be observed from the derivation that Equation (9) is applicable to conductive films with finite\nboundaries. This confirms that the model aligns well with the physical reality, and consequently, the\nlocalization method derived from this theoretical framework does not introduce significant edge effects.”\nOn page 13, lines 23 to 37:“ To assess homogeneity of the fabricated film at the micro-scale, we\nperformed SEM on our conductive film. Two random samples (0.5 cm × 0.5 cm each) were taken from\na large-area film (10 cm × 10 cm) prepared by electrospinning. As shown in Fig. 3i and Supplementary\nFigure 16a-e, the SEM results clearly demonstrate that the electrospinning process successfully produced\na dense and uniformly distributed nanomesh structure. Furthermore, we measured the thickness profile\nof the nanomesh through AFM (Supplementary Figure 16f-g). The data showed a maximum height\nvariation of less than 1.5 µm, which is negligible for a film with a total thickness exceeding 100 µm,\nand an average roughness (Ra) of 150 nm. These results provide strong evidence of high structural uniformity\nat the microscopic level. Furthermore, the isoline of the nanomesh membrane, as depicted in\nSupplementary Figure 15b(ii), demonstrates much more continuous and stable characteristics compared\nto that obtained via the solution method (Supplementary Figure 15a(ii)), attesting to its exceptional\nuniformity. The result demonstrates that nanotextured interface engineering (nanomesh structure) can\nreconcile conflicting requirements of high uniformity and large-area fabrication.”\n4. Pages 14. Emotional labels. Labels were obtained via self-assessment alone, without cross-checking\nwith physiological sensors, which introduces subjective bias. It could have been worth including some\nwell-recognised physiological validation (e.g., GSR, ECG) to corroborate emotional states. Moreover,\ngesture force and velocity are confounded in the pulse output (Fig.4c)—without controlled mechanical\nstimulation, feature attribution remains uncertain.\nResponse to reviewer:\nWe sincerely thank the reviewer for raising these critical points regarding the subjective bias in emotional\nlabeling and the potential confounding of force and velocity in the pulse output. We have carefully\nconsidered these concerns and provide the following clarifications and additional justifications based on\nestablished methodologies and our experimental design.\n1. Justification for the Self-Assessment Manikin (SAM) and Video-Based Emotion Elicitation\nThe use of the Self-Assessment Manikin (SAM) scale for assessing emotional arousal and valence is\nwidely regarded as a standard in emotion research due to its standardized and quantifiable nature6-9.\nCoupled with video-based emotion elicitation—a highly controlled and objective method commonly\nemployed in the field—our approach minimizes subjective imagination bias and ensures consistent\nemotional induction across participants.\nCurrently, multiple techniques exist for emotion recognition, including but not limited to: facial\nexpression capture, monitoring physiological signals such as electrocardiogram (ECG), respiration\n(RSP), galvanic skin response (GSR), electromyography (EMG), and electroencephalogram (EEG),\nelectrochemical detection of bodily secretions, and affective touch-based sensing. As summarized in\nTable R8, which compares features of state-of-the-art works in emotion recognition, video-based\nelicitation coupled with specialized questionnaires remains a mainstream methodology. However, the\naccuracy of emotion measurement varies significantly across different techniques, and a universally high-\naccuracy approach is still lacking, limiting the reference value for standardized assessment.\nNotably, our work is among the first to integrate affective touch with video-based elicitation and SAM-\nbased assessment, addressing a gap in the existing literature where affective touch studies often rely on\ngesture control or subjective imagination without standardized emotional validation.\nTable R8 State-of-the-art works in emotion recognition.\nWay to elicit Emotion assessment Accuracy\nWorks Method\nemotion method (%)\nLee et al.32 Facial detection Expression control N/A 93.33\nLi et al.33 Facial detection Expression control N/A 86.7\nFacial\nDu et al.34 electromyography Photographs N/A 93\n(fEMG)\nQuestionaire\n68.30-\nWu et al.35 GSR Video clips (Emotion state and\n96.00\nstrength)\n73.71-\nJing et al.36 ECG Video clips Questionaire\n94.59\nQuestions following\nZheng et al.37 EEG Video clips 86.65\nPhilippot38\nEEG 72.48\nSong et al.39 Video clips\nGSR 60.24\nRSP Questionnaires 56.17\n(PANA, SAM,\nECG 53.20\nDES*)\nChoi et al.40 Affective touch Gesture control N/A N/A\nSubjective\nLi et al.41 Affective touch N/A 72.47\nimagination\nThis work Affective touch Video clips Questionaire (SAM) 82.37\n* PANA: Positive and negative affective scheme42, SAM: Self-assessment manikin, DES: Differential\nemotion scale43.\n2. Addressing Force and Velocity Confounding in Pulse Output\nFig. R32 a COMSOL simulation results of the process illustrating the dynamic deformation of two\nhairs on the interface when a finger slides over the interface (S -S ). b Schematic diagram of the\n1 5\ndynamic variation of the induced charge process. c (i) Contact area acquired from the simulation in (b).\n(ii) Measured transferred charge. (iii) Corresponding calculated voltage output.\nWe acknowledge the reviewer’s concern regarding the potential confounding of force and velocity in the\ntriboelectric pulse signal. However, our system is designed to decouple these parameters through distinct\nphysical mechanisms and signal processing steps. As derived from the charge equation 𝑄 =∫𝐼∙𝑑𝑡 =\n∫(𝐼 +𝐼 +𝐼 +𝐼 )𝑑𝑡 =∫\n𝑉1+𝑉2+𝑉3+𝑉4𝑑𝑡,\nthe force (or intensity) of interaction is proportional to the\n1 2 3 4\n𝑅𝑒𝑥\nintegrated charge over time, which is directly reflected in the voltage integral across electrodes. In\ncontrast, the velocity of the stroking motion correlates with the frequency of contact-separation events,\nas confirmed by our simulation analysis (Fig. R32, original Fig. 2b-d). Specifically, the number of pulse\npeaks is determined by the rate of contact-separation cycles (governed by velocity) and is independent\nof the applied force. During network training, these features are explicitly separated: the force\ninformation is encoded as the charge integral (from voltage summation), while velocity-related frequency\ninformation is extracted via peak frequency calculation. This theoretical and algorithmic decoupling\nensures that the two dimensions do not interfere in feature attribution.\nIn summary, our methodology for emotional labeling aligns with established practices in the field, and\nour feature extraction framework explicitly accounts for the independence of force and velocity. We\nappreciate the reviewer’s insightful comments and will further emphasize these justifications in the\nrevised manuscript.\n5. Page 13-15, Fig. 4a-d. The selection of neural networks used (1D CNN-LSTM for gestures, standard\nCNN for emotion) is not fully justified. For instance, there is no comparison with alternative architectures\n(e.g., Transformers, ResNet, TCNs), nor are ablation studies presented to validate their effectiveness. The\nmodel’s interpretability is also lacking—no insights are provided into what features drive classification\ndecisions. It is suggested that the authors consider adding to the Supplementary Figure 9-12 some cross-\narchitecture comparison and ablation studies.\nResponse to reviewer:\nWe sincerely thank the reviewer for this insightful comment regarding the justification of our neural\nnetwork architectures and the need for comparative and ablation studies. We have now conducted\ncomprehensive cross-architecture comparisons and ablation experiments to rigorously validate our\ndesign choices.\nFor pure gesture recognition, we acknowledge that many advanced architectures can achieve excellent\naccuracy because the signal features are relatively distinct. Our initial choice of the 1D CNN-LSTM\nensemble was based on its ability to capture both spatial features and temporal dependencies. However,\nas suggested, we compared it with Temporal Convolutional Networks (TCNs). The results confirmed\nthat while both models could achieve high accuracy (e.g., over 99.5%). However, 1D CNN-LSTM takes\n0.0042s to predict a sample while TCNs takes 0.0215s. Thus, 1D CNN-LSTM is preferred in this case\ndue to its high efficiency.\nFor emotion recognition, our use of a CNN based on a ResNet architecture was driven by our strategy\nto convert the sequential signal analysis into an image classification problem. We achieved this by\ngenerating Continuous Wavelet Transform (CWT) time-frequency spectrograms from the raw data. The\nResNet architecture is particularly well-suited for this task due to its strong performance in extracting\nhierarchical spatial features from images. To validate this, we conducted comparisons with TCNs (which\nprocess the data as a 1D sequence) and Transformer architectures. The results, as hinted in the Table R9,\nshowed that the ResNet-based CNN achieved superior performance on our CWT image dataset compared\nto the other architectures.\nFurthermore, we performed extensive ablation studies to understand the contribution of different input\ndata representations and features. We compared models trained on raw temporal data, CWT images, and\nspecifically evaluated the importance of frequency information versus force information embedded\nwithin the CWT images. The findings revealed that frequency information was vital for both tasks.\nInterestingly, force information, while slightly reducing the performance on the isolated gesture\nrecognition task, provided a substantial positive contribution to the accuracy of emotion recognition. This\nindicates that the physical force of an interaction carries important affective cues. The model utilizing\nthe combined frequency and force information achieved the highest overall emotion recognition\nperformance.\nIn conclusion, the additional experiments fully justify our architectural selections. For gesture\nrecognition, 1D CNN-LSTM is preferred for its high efficiency. For emotion recognition, the ResNet-\nbased CNN processing CWT images is demonstrably superior for our specific data representation, and\nthe ablation studies provide clear insights into the features driving the classification decisions.\nTable R9 Comparison between different networks on emotion recognition.\nAction Affection Total\nMethod recognition recognition recognition\naccuracy accuracy accuracy\n1D CNN & LSTM (Waveform with voltage and\n83.54% 64.13% 61.56%\ncharge information)\nTCNs (Waveform with voltage and charge\n94.89% 73.11% 71.14%\ninformation)\nResNet (CWT image only) 98.46% 75.67% 75.67%\nResNet (CWT image with force information) 97.46% 79.38% 78.46%\nResNet (CWT image with frequency information) 99.29% 79.50% 79.13%\nResNet (CWT image with frequency and force\n98.92% 82.37% 81.92%\ninformation)\nTransformer (CWT image with frequency and force\n96.42% 77.21% 76.62%\ninformation)\nOn page 20, lines 10 to 33: “Then the pictures are inputted into a neural network for training (Fig. 5a(v)).\nSince the gesture characteristics of different people vary greatly, the network is trained through leave-\none-subject-out cross-validation (LOSOCV) to ensure its validity. The tactile data of a single volunteer\nis isolated as a test set to avoid overfitting the network to the collected data.\nTo determine the optimal network architecture for emotion recognition, we conducted comparative\nanalyses of several deep learning models. Given our strategy of converting sequential signals into an\nimage classification task, a ResNet-based CNN architecture was selected for its demonstrated excellence\nin extracting hierarchical spatial features from image-like data. As shown in Supplementary Figure 27a\nand Supplementary Table 4, the accuracy of the neural network with different structures is compared.\nComparative experiments with 1D CNN-LSTM, temporal convolutional networks (TCNs), which\nprocess data as 1D sequences, and transformer architectures, revealed that the ResNet-based CNN\nachieved superior performance on our dataset, as supported by the results summarized. Furthermore,\nextensive ablation studies were performed to dissect the contribution of different input representations\nand features (Fig. 5d, Supplementary Figure 27b and Supplementary Table 4). These studies evaluated\nmodels trained on CWT images, and specifically isolated the impact of frequency information and force\ninformation embedded within the CWT spectrograms. The findings unequivocally demonstrated that\nwhile frequency information was crucial for both gesture and emotion recognition tasks, the inclusion of\nforce information provided a substantial positive boost to emotion recognition accuracy, underscoring\nthat the physical intensity of an interaction carries critical affective cues. Consequently, the model\nleveraging combined frequency and intensity information within the ResNet-based CNN framework\nachieved the highest overall emotion recognition performance, fully justifying our architectural and\nfeature selection.”\n6. It is also recommended to include model interpretability data and information such as Grad-CAM,\nsaliency maps, or attention weights, to inform what features the CNN attends to for classification, for\ninstance.\nResponse to reviewer:\nWe thank the reviewer for the valuable suggestion to enhance the interpretability of our CNN model.\nIn response, we have conducted a comprehensive Grad-CAM analysis to elucidate the specific image\nfeatures that our network attends to when making classification decisions for both gestures and emotions.\n1. Dominance of high-frequency features\nThe Grad-CAM results consistently revealed that high-frequency components within the CWT\nspectrograms were the most salient features for our network. As shown in the Fig. R33, the heatmaps\nintensely highlight regions corresponding to these high-frequency elements across all eight gestures and\nthree emotion labels. This strongly indicates that the transient, rapid changes in the triboelectric signal—\nwhich are captured as high-frequency content—are the most discriminative cues for the model. This\nfinding is physiologically plausible, as the spiking dynamics of bio-inspired sensors and the rapid onset\nof tactile gestures are inherently encoded in high-frequency signal variations.\n2. Contribution of pressure information\nFor a subset of gestures, particularly those where intensity is a key differentiator (such as press, stroke,\npush), the Grad-CAM heatmaps also showed significant activation in regions of the pressure information.\nIn the context of emotion recognition, the integration of pressure information is critical, as the force of a\ntouch (e.g., a gentle stroke vs. a firm stroke) can convey vastly different emotional intentions.\n3. Attention to frequency characteristics\nIn gestures characterized by sustained and specific spectral qualities, such as stroke and tickle, the model\nalso allocated attention to specific frequency bands or spectral patterns. It is noteworthy that these two\ngestures exhibit the most pronounced frequency variations under different emotional states, and the\nnetwork effectively captures these differences to inform emotion analysis.\nIn conclusion, the Grad-CAM analysis provides transparent and compelling evidence that our CNN\nmodel learns to focus on multiple meaningful features in the data. The attention to high-frequency\ndynamics, pressure intensity, and frequency characteristics aligns well with the expected principles of\ntactile information processing. This deepens our understanding of how the neuromimetic signals can be\ndecoded to infer human gestures and emotions.\nFig. R33 Original image input with different gestures and emotion label and their corresponding\nGrad-CAM for the applied CNN.\nOn page 21, lines 15 to 31: “To further interpret the decision-making process of the CNN model, we\nperformed Gradient-weighted class activation mapping (Grad-CAM) analysis. The Grad-CAM results\nconsistently revealed that high-frequency components within the CWT spectrograms were the most\ndiscriminative features across all eight gestures and three emotion categories. As illustrated in the\nactivation heatmaps (see Supplementary Figure 29), the model intensely attended to regions\ncorresponding to transient, rapid changes in the triboelectric signal, suggesting that high-frequency\ndynamics are critical cues for both gesture identification and emotion inference. This observation aligns\nwith the neurophysiological principle that affective touch perception is often encoded in rapid tactile\ndynamics. In gestures where force serves as a key differentiating factor, such as press, stroke, and push,\nthe Grad-CAM heatmaps also showed significant activation in regions encoding force information. This\nunderscores the model’s ability to incorporate force-related cues when distinguishing between\nemotionally or physically distinct tactile interactions. Moreover, for gestures characterized by sustained\nspectral patterns, such as stroke and tickle, the network allocated additional attention to specific\nfrequency bands, indicating its sensitivity to spectral shapes in emotional tactile expression. These visual\nexplanations corroborate the quantitative performance observed in Fig. 5g, where gestures exhibiting\nricher feature variations (e.g., stroke) achieved higher emotion recognition accuracy.”\n7. Page 16-18 “Close loop of natural emotional interaction integrated with Chat Generative Pre-trained\nTransformer (ChatGPT)” and Supplementary Figure 14-15. Please provide quantitative analysis and\ncomparison of the selected transformer network model with traditional dialogue systems or user studies.\nResponse to reviewer:\nThank you for this comment. While the primary focus of our work is not on developing sentiment analysis\nmodels, we agree that a quantitative justification for selecting the ChatGPT model strengthens our\nmethodological foundation. Based on existing literature, we can demonstrate the superior capability of\nadvanced Large Language Models (LLMs) like GPT in sentiment understanding compared to traditional\nsystems, which aligns with our goal of creating a natural and intelligent emotional interaction loop.\nThe key advantage of using ChatGPT lies in its powerful zero-shot or few-shot learning capabilities,\neliminating the need for extensive, task-specific fine-tuning on sentiment data that traditional models\nrequire44. This allows our system to handle the nuances and unpredictability of natural emotional\ninteraction flexibly.\nLLMs like ChatGPT has demonstrated its excellent emotion detection capabilities without specialized\ntraining. David et al. conducted a comparative analysis of GPT-3.5 and IBM Watson for sentiment\nanalysis and emotion classification using a database of 30,000 COVID-19-related tweets. The results\nshowed that GPT-3.5’s contextual prompting approach enabled it to better understand the emotion of\nsarcasm, despite not having explicit training in sentiment analysis. (Comparative Study of Large\nLanguage Models as Emotion and Sentiment Analysis Systems: A Case-Specific Analysis of GPT vs.\nIBM Watson)\nAlthough LLMs possess zero-shot capabilities, some studies indicate that they not only compete with\ntraditional transfer learning methods in sentiment classification accuracy but can even surpass them in\ncertain cases. Jan et al. evaluated the performance of the high-performance transfer model RoBERTa and\nthree state-of-the-art LLMs—GPT-3.5, GPT-4, and Llama 2—as zero-shot sentiment analyzers for three-\nclass sentiment classification tasks18. The dataset consisted of 792 text documents covering product\nreviews from Amazon, Flipkart, and Twitter.\nTable R10 presents the classification accuracy of several models in the three-class sentiment\nclassification experiment. Overall, GPT-4 achieved the highest classification accuracy in three out of four\ndatasets, with an average performance at least 4.9 percentage points higher than other LLMs and\nRoBERTa. GPT-3.5 and RoBERTa showed similar results, each recording an average accuracy of\napproximately 78%. RoBERTa, which was specifically fine-tuned on a corpus containing over 5,000\nsocial media posts, demonstrated the highest accuracy in one of the Twitter datasets (see Table R10,\nTwitter Various II). This enhanced performance can be attributed to its training on data similar to the\ntested Twitter dataset, highlighting the advantage of model specialization. The experimental results\nunderscore the outstanding zero-shot performance of LLMs in sentiment analysis. However, they also\nemphasize that specialized, fine-tuned transfer learning models like RoBERTa can outperform LLMs in\ncertain scenarios.\nTable R10 Three-class sentiment classification accuracy for RoBERTa and LLMs18.\nTransfer Learning\nLLMs (zero-shot)\nModel (fine-tuned)\nRoBERTa GPT-3.5 GPT-4 Llama 2\nAmazon (iPhone) 73.2 85.4 88.9 87.9\nFlipkart (Various) 78.3 91.9 92.4 90.9\nTwitter (Airlines II) 72.7 69.2 77.8 67.7\nTwitter (Various II) 86.4 60.1 71.2 56.1\nAverage 77.7 77.7 82.6 75.7\nWithout additional training, Jeana19 compared the accuracy of a lexicon-based model (VADER), a deep\nlearning Transformer-based model (Twitter-roBERTa-base), and a GPT-based model (GPT-4o mini) for\nsentiment analysis. The dataset used was an energy transition-related tweet corpus containing 1,000\nmanually annotated English tweets as a benchmark for comparison. Table R11 shows the comparative\nperformance of the sentiment classification models, with the GPT-based model outperforming the other\ntwo methods. It further emphasizes the high performance of GPT in a zero-shot setting.\nTable R11 Performance comparison across different sentiment classification models19.\nAccuracy\nModel Precision (%) Recall (%) F1-score (%)\n(%)\nVADER (Lexicon-\nbased) 65 (92, 52, 64) (27, 35, 93) (42, 42, 76)\nTwitter-RoBERTa-Base 76 (97, 48, 93) (52, 85, 84) (67, 62, 88)\nGPT-4o mini 83 (99, 82, 78) (87, 38, 100) (92, 52, 88)\nWhy GPT is superior for natural emotional interaction\nOur choice of GPT over traditional dialogue systems is attributed to two key reasons that are central to\nour innovation:\n1. Contextual understanding and flexibility: Unlike traditional rule-based or simple classifier-based\nsystems, GPT demonstrates a remarkable ability to grasp nuance, sarcasm, and context. In our system,\nthis translates to the AI understanding the emotional intent behind a user’s action based on the specific\nscenario. As illustrated in Fig. R34, the same physical interaction (e.g., a tickle) can be interpreted\ndifferently by ChatGPT depending on the context (e.g., first meet vs. stay for a long time), leading to\ndiverse and appropriate emotional feedback from the robotic dog. This flexibility is a cornerstone of\nnatural interaction.\nFig. R34. Flowchart illustrating the impact of the context information on the understanding and\nexpression of emotions. Here, two different context information (scenario, characteristics of the dog,\nand the relationship between human and dog) were set and the same interaction (tickle) was applied to\nthe BioAI2. The ChatGPT presented different emotion understanding and executed different actions to\nexpress different emotions.\nFig. R35 Chord diagram describing the likelihood of different dog actions chosen by ChatGPT\nunder different emotion states of humans.\n2. Intelligent decision-making vs. pre-programmed responses: Traditional systems often rely on a\none-to-one mapping of input to a pre-defined output, making interactions predictable and rigid. In\ncontrast, ChatGPT’s language understanding enables it to generate intelligent, autonomous decisions on\nhow to respond emotionally. This allows for unpredictable, lifelike, and more engaging communication,\nmoving beyond stale, programmatic interactions. Specifically, the robot dog can utilize any combination\nof its 10 distinct actions (Fig. R35, original Fig. 5d) for emotional feedback. Even when limiting the\nnumber of actions per response to three, the system can produce up to 10 (for one action) + 109 (for two\nactions) + 109×8 (for three actions) = 820 distinct combinations, ensuring significant variability and\nunpredictability. As summarized in Table R12, which compares several e-skin-based emotional HRI\nworks, our ChatGPT-based robot dog demonstrates the highest level of intelligence by effectively\ncontextualizing information and diversifying response types.\nTable R12 E-skin based human-robot interaction\nIf consider\nInteraction If detect Possible types\nWorks contextual Decision making\nobjects emotion of responses\ninformation\nProgram\nDu et al.34 Robot car Yes No 6\npresetting\nRobot dog\nProgram\nHuang et al.45 and virtual Yes No 7\npresetting\ncharacter\nProgram\nChoi et al.40 Virtual robot Yes No 4\npresetting\nVirtual Program\nLee et al.32 Yes Yes N/A\ncharacter presetting\nChatGPT-based\nThis work Robot dog Yes Yes independent >820\ndecision making\nOn page 23, lines 17 to 31:“Specifically, the ChatGPT is integrated into this emotional interaction\nframework primarily due to its advanced reasoning capability, which enables context-aware\nunderstanding and appropriate response generation. It excels by dynamically incorporating contextual\ncues into its inference process, adapting responses based on the interaction setting, user identity, and\nemotional history to ensure relevance and interpersonal appropriateness. Moreover, its ability to\ncomprehend through dialogue and generate stable, emotionally congruent text makes it highly suitable\nfor real-time emotional interaction loops, supporting consistent affective expression. Compared to other\nTransformer architectures, such as BERT, which excels in comprehension tasks like sentiment\nclassification when fine-tuned on specific datasets, GPT provides distinct advantages in zero-shot and\nfew-shot scenarios where extensive task-specific training is not feasible66,67 (Supplementary Note 7).\nThis robust few-shot and zero-shot learning capability, stemming from its generative pre-training and\nautoregressive design, makes GPT particularly suitable for the dynamic and evolving context of natural\nemotional interaction proposed in our study. The entire process has been depicted in a flowchart as shown\nin Fig. 6b.”\nAnd from page 24, line 28 to page 25, line 5: “The integration of ChatGPT is pivotal for achieving natural\nemotional interaction, primarily due to its superior contextual understanding and flexible decision-\nmaking capabilities, which distinguish it from traditional rule-based or simple classifier-based systems.\nChatGPT demonstrates a remarkable ability to grasp nuance and interpret emotional intent based on the\nspecific scenario. For example, we implement another experiment that applied the same interaction\ngesture (tickle) on the BioAI2 without considering the emotional information extracted from the gesture\nwas also performed (as shown in Supplementary Figure 32 and Supplementary Video 4). The ChatGPT\npresents a completely different emotion understanding and execute diverse motion feedback under\ndifferent scenarios, characteristics of the dog and their relationship. The result indicates the significant\nimpact of contextual information on ChatGPT’s decision during emotional interaction. Unlike systems\nthat rely on a one-to-one mapping of input to a pre-defined output, resulting in predictable and rigid\ninteractions, ChatGPT’s language understanding enables it to generate intelligent, autonomous decisions\non how to respond emotionally. This allows for unpredictable, lifelike, and more engaging\ncommunication, moving beyond stale, programmatic interactions. Specifically, the robot dog can utilize\nany combination of its 10 distinct actions (Fig. 6d) for emotional feedback. Even when limiting the\nnumber of actions per response to three, the system can produce up to 10 (for one action) + 109 (for two\nactions) + 109×8 (for three actions) = 820 distinct combinations, ensuring significant variability and\nunpredictability. As summarized in Supplementary Table 6, which compares several e-skin-based\nemotional HRI works, our ChatGPT-based robot dog demonstrates the highest level of intelligence by\neffectively contextualizing information and diversifying response types.”\nComment 8: The manuscript is affected by several typos and language errors. It is recommended to\nproofread it before resubmission.\nResponse to reviewer:\nWe thank the reviewer for this important observation and for the careful review of our manuscript. We\nsincerely apologize for the typos and the lack of clarity in the English expression, which affected the\nquality of the initial submission. We have conducted a thorough, line-by-line proofreading of the entire\nmanuscript to identify and correct all typographical errors. All changes have been implemented in the\nrevised manuscript, which we believe now meets the high standards of academic writing required for\npublication.\nReviewer #3 (Remarks on code availability):\nThe code is provided in github. However, access to the dataset requires specific request and approval to\naccess the same from a Google Drive link.\nResponse to reviewer:\nWe sincerely apologize for the access issues encountered with the dataset in the original submission.\nThank you for highlighting this point. To ensure full reproducibility and ease of access, we have now\nmigrated the complete dataset to the new GitHub repository (https://github.com/Jairo-H/BioAI2_code)\nthat hosts the code. The dataset is now publicly available and can be directly accessed without any\npermissions or approvals required. The Google Drive link referenced in the previous version has been\ndeactivated.\nWe regret any inconvenience this may have caused and appreciate you bringing it to our attention. We\nbelieve this change significantly improves the accessibility and reproducibility of our work.\nReference:\nfibres that detect massage-like stroking of hairy skin in vivo. Nature 493, 669-673 (2013).\nhttps://doi.org:10.1038/nature11810\nInitiated Touch. IEEE Trans. Affective Comput. 13, 1477-1488 (2022).\nhttps://doi.org:10.1109/TAFFC.2020.3008693\ndistinct emotions. Emotion 6, 528-533 (2006). https://doi.org:10.1037/1528-3542.6.3.528\ndifferential. J. Behav. Ther. Exp. Psy. 25, 49-59 (1994). https://doi.org:10.1016/0005-\n7916(94)90063-9\nTrans. Affective Comput. 13, 1541-1554 (2022). https://doi.org:10.1109/TAFFC.2020.3014842\nperceptions of virtual humans’ emotional expression on social media. Comput. Hum. Behav. 155,\n108161 (2024). https://doi.org:10.1016/j.chb.2024.108161\nresponses: A personalized assessment based on heartbeat dynamics. Sci. Rep. 4, 4998 (2014).\nhttps://doi.org:10.1038/srep04998\ncare products. Int. J. Ind. 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Psychol. 54, 1063-1070 (1988).\nhttps://doi.org:10.1037/0022-3514.54.6.1063\nhttps://doi.org:10.1080/02699939508408966\nSpatiotemporal and Mechanical Features of Social Touch to Enhance Human–Robot Emotion\nRecognition. SmartMat 6, e1325 (2025). https://doi.org:10.1002/smm2.1325"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The manuscript presents a biomimetic hairy tactile interface (BioAI²) that integrates triboelectric sensing, hierarchical structural design, and hybrid neural networks for affective touch recognition and human-robot emotional interaction. Previous questions/concerns regarding the details of the methodology were responded with very lengthy discussion and certain revisions have been made. Although the title seems interesting, the material design, sensing mechanisms, and algorithm in this work have already been reported before, and this work still failed to provide a comprehensive and in-depth elaboration on the core novelty and differences from existing similar studies as well as the unique value brought by these differences.\n（1）Triboelectric Sensing Mechanism\nPlease provide a comparative analysis to clarify two key differences between BioAI² and the TENG-DARS platform (Nano Energy 131, 2024, 110327，previous work of the author). This sensing mechanism and structure have also been extensively reported before by many research groups before. What is the core difference in their triboelectric-based sensing mechanisms and how does this affect the sensing process?\n(2) Limited Materials and Structural Innovation\nThe core materials (Ecoflex, Ag electrodes) and the “bistage hierarchical hairy structure” are incremental modifications of existing designs in triboelectric tactile sensors (such as Adv. Sci. 2025, e10741; Nano Energy 2019, 57, 424). The manuscript fails to demonstrate fundamental advances in material composition, structural optimization, or processing technology that distinguishes it from prior works.\n（3）Unquantified Fusion Mechanism Between CNN and LLM for Emotional Interaction\nThe manuscript claims to integrate a ResNet-based CNN (for emotion recognition, 82.37% accuracy) with ChatGPT (for contextual emotional understanding) to form a closed-loop interaction. However, it fails to clarify how tactile features extracted by the CNN (e.g., spike frequency, force magnitude) are quantitatively mapped to ChatGPT’s input, and how conflicts between \"tactile emotion cues\" and “contextual information” (e.g., a “gentle stroke” but negative scenario context) are resolved. The fusion process remains a “black box”, lacking experimental validation of the contribution of each component.", "reviewer_3": "The authors have addressed all reviewers comments with sufficient depth and improved explanations, including with additional notes in the supplementary information file and in the amended manuscript. As a result the overall quality of the manuscript has also improved. The link to the code in github now works and additiona supplementary information\nI have no further comments. Thank you for the work done.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response to reviewer:\nWe sincerely thank the reviewer for this insightful comment. We acknowledge the concern regarding\nthe elaboration of our core novelty relative to existing studies. Please allow us to clarify the\nfundamental advancements presented in our work, which collectively represent a significant step\nbeyond prior art. The novelty of BioAI² is not merely incremental but lies in the first-time integration\nand demonstration of several key principles within a unified biomimetic tactile interface for\naffective touch perception.\nSpecifically, our manuscript provides the following unique contributions:\n1. A novel mathematical framework for artifact-free sliding trajectory reconstruction: Existing\nmethods for tactile trajectory tracking often suffer from significant edge distortions (Fig. R1(i)). Our\nwork is the first to present a complete mathematical theory that thoroughly models these edge\neffects inherent in traditional positioning methods. Building on this theoretical foundation, we\nintroduced an isoline theory specifically designed to counteract the edge effect, achieving high-\nprecision, distortion-free trajectory reconstruction (Fig. R1(ii)). This provides a fundamentally more\naccurate basis for analyzing continuous sliding motions.\nFig. R1 Spatially resolved position error from (i) the traditional ratio method and (ii) the proposed\nisoline method.\n2. A breakthrough in material engineering for enhanced signal uniformity: To optimize\npositioning accuracy and signal stability, we pioneered the use of PVC-DBA as an electrostatic\nspinning material for fabricating highly uniform conductive nanomesh films. This innovation\nresults in an exceptionally consistent and homogeneous conductive layer, which is crucial for\nminimizing positioning errors and ensuring the reliability of the acquired tactile signals. The\nperformance of the BioAI2 is also compared to state-of-the-art soft tactile sensors as shown in Table\nR1 (also in Supplementary Table 1).\n3. First demonstration of multi-finger continuous sliding perception based on four-electrode-\ndesign with neuromimetic pulse output: While four-electrode-design1-3 is known, its application\nhas been largely limited to single-point or discrete contact sensing. Our work is the first to realize\ncontinuous sliding trajectory localization across multiple fingers using the TENG principle.\nMore importantly, the generated triboelectric signals are unique neuro-mimetic pulse outputs that\nsimultaneously encode multi-dimensional information including position, force, and, critically,\nemotional cues within the same signal stream. This integrated encoding is a key differentiator.\n4. Emulation of C-LTMR-like neural response for quantifiable affective output: A cornerstone\nof our affective touch recognition claim is the successful reproduction of C-LTMR-like neural\nspiking patterns. Specifically, our interface exhibits a characteristic inverted U-shaped\nfrequency-response relationship to sliding velocity, mirroring the biological response associated\nwith pleasant touch. This represents the first provision of a quantifiable, neuro-inspired\nfrequency output that directly encodes tactile affective information, moving beyond simple\nforce or texture discrimination.\nIn summary, the unique value of BioAI² stems from being the first system to synergistically combine\na theoretically grounded solution for accurate sliding tracking, a novel material platform for signal\nuniformity, multi-finger continuous perception with rich neuro-mimetic output, and a biomimetic\naffective encoding scheme. This integration enables a previously unattained capability for nuanced\nhuman-robot emotional interaction based on quantifiable affective touch signatures.\nTable R1. State-of-the-art soft electronics for tactile mapping interface\nWorks Methods Inter- Energy Multi- Sensing Electrode Pixel Temporal Sensing\nconnect supply point area number number resolution range\nfree free mapping (mm2) /refresh\nrate\nKim et al.4 Multiscale × × O 25 6 25 24 ms 16-80 kPa\nstructures\nOh et al.5 Transistor × × O 600 25 128 10 ms 0-5 kPa\narrays\nHuang et Capacitive × × O 625 10 25 N/A 20-35 kPa\nal.6 sensor arrays\nZhong et Transistor × × O 8 >20 200 N/A N/A\nal.7 arrays\nShi et al.8 Capacitive × × O 10,000 56 784 120 Hz 0.15-440\nsensor arrays kPa\nPark et al.9 EIT, PAT × × O 40,000 32(EIT) ~2268* N/A N/A\n+25(PAT)\nCho et al.10 Resistive and O × × 10,000 2 16,891 N/A N/A\ncapacitive\nLee et al.11 Triboresistive O O × 1,200 4 9 N/A N/A\nXu et al.2 Resistive O × × 10,000 4 100 70 ms N/A\nXia et al.12 Resistive O O × 3,600 4 49 N/A N/A\nKim et EIT O × O 314 16 >920 N/A 3.2-159 kPa\nal.13\nMassari et Fibre Bragg O × O >10,000 16 16 N/A 0-2.5 N\nal.14 grating.\nOur work Biomimetic O O O 10,000 4 3858* 5 ms 0-50 N\nhairy based\ntriboresistive\nO denotes Yes, × denotes No.\n* Calculated by S/(δ)2, S denotes the sensing area, δ denotes root mean square (RMS) error.\nEIT: Electrical impedance tomography.\nPAT: Passive acoustic tomography.\nWe have revised the manuscript to elaborate these points more clearly and thank the reviewer for\nprompting this crucial clarification.\n（1）Triboelectric Sensing Mechanism\nPlease provide a comparative analysis to clarify two key differences between BioAI² and the TENG-\nDARS platform (Nano Energy 131, 2024, 110327，previous work of the author). This sensing\nmechanism and structure have also been extensively reported before by many research groups\nbefore. What is the core difference in their triboelectric-based sensing mechanisms and how does\nthis affect the sensing process?\nResponse to reviewer:\nThank you for this insightful comment regarding the comparison between BioAI² and the TENG-\nDARS platform (Nano Energy 131, 2024, 110327). We agree that the triboelectric effect is a widely\nutilized sensing mechanism, and its specific implementation—rather than the core effect itself—\ndetermines the novelty and application potential.\nBelow, we provide a comparative analysis focusing on two key differences in the sensing\nmechanisms and how they impact the sensing process.\n1. Fundamental sensing mechanism: resistive sensing vs. intrinsic triboelectric encoding\nThe TENG-DARS platform primarily relies on a resistive sensing mechanism. Although it uses a\nfreestanding-mode TENG to generate voltage output for powering the system, the actual sensing of\nphysical parameters is achieved through resistive sensors whose resistance changes in response to\nexternal stimuli. The TENG here acts mainly as an energy harvester, and the sensing information is\ndecoded by monitoring the resistance variations in parallel circuits. Here, the TENG can be replaced\nby other generators, such as PENG, pyroelectric generator, magnetoelectric generator. In contrast,\nBioAI² employs a direct triboelectric encoding mechanism where the tactile information (e.g.,\nposition, force, speed) is intrinsically encoded into the dynamic characteristics of the triboelectric\nsignals generated by the biomimetic hair structure. This mechanism leverages the resistive\ntopology of a uniform plane, the interplay between contact-separation charge transfer, and\nthe kinematic relationship between the hair and sliding finger to directly transduce multi-modal\ntactile features without secondary transduction elements. The triboelectric mechanism is crucial and\nirreplaceable in this design. This difference makes BioAI² a more direct and biologically inspired\napproach, similar to how biological mechanoreceptors encode tactile information.\n2. Signal generation and sensing process: continuous sinusoidal vs. spike-based neuromorphic\noutput\nIn the TENG-DARS platform, the sensing process involves a continuous sinusoidal output from\nthe freestanding TENG, which powers multiple resistive sensors. The physical information is\nextracted by analyzing the circuit's response (e.g., voltage division) to resistance changes, which\nrequires sophisticated circuit design and power management for multi-modal sensing. BioAI²,\nhowever, generates spike-like neuromorphic pulses during finger-hair interaction, mimicking the\nneural spiking patterns of biological tactile nerves (e.g., C-LTMRs). Each contact-separation event\nproduces a discrete pulse whose frequency and amplitude directly correlate with sliding velocity\nand force, enabling real-time, event-driven perception without external power or complex circuitry.\nThis spike-based encoding allows BioAI² to achieve high temporal resolution and natural\ncompatibility with neural network processing for emotion recognition, as demonstrated by its\ninverted U-shaped response to stroking velocity—a hallmark of biological affective touch.\nTable R2 briefly summarizes the differences between TENG-DARS and BioAI2.\nTable R2 Differences between TENG-DARS and BioAI2\nWorks TENG-DARS BioAI2\nTENG mode Freestanding mode Single electrode mode\nTriboelectric Sinusoidal voltage output Neuromimetic spike-like output\noutput type\nSensing Triboelectric-powered resistive sensor Biomimetic hairy based triboelectric\nmechanism\nSensing modals Strain, temperature, pressure Position, pressure, speed\nComplexity Require complex circuitry Do not require extra circuitry\nSupporting evidence: diversity of triboelectric applications\nThe versatility of the triboelectric effect is evident in numerous high-impact studies where\nmodifications in material, structure or application yield distinct functionalities15-17. For example,\nShao et al.18 proposed a large-area multiplexing self-powered untethered triboelectric electronic skin\nusing a stretchable carbon black-Ecoflex composite, enabling the self-powered e-skin to operate\nreliably under extreme strains (up to 400%) while providing high-quality pressure imaging and\nmulti-touch visualization for applications like smart gloves and insoles. (Nat. Commun. 15, 2024,\n1238) He et al.19 uses a hemispherical triboelectric sensor array to recognize material type and\nsoftness with >99% accuracy by leveraging material deformability and electron affinity. (Sci. Adv.\n10, 2024, eado6793) Du et al.20 integrated a 2D triboelectric sensor array with deep learning\nalgorithms, achieving a 99.56% accuracy in material identification, demonstrating precise remote\ncontrol capabilities for robotic manipulators. (Sci. Adv. 10, 2024, eadp8681)\nThese examples underscore that the core innovation lies not in the triboelectric effect itself, but in\nhow it is engineered for specific sensing paradigms. BioAI2 distinguishes itself through its\nbiomimetic structure and neuromorphic encoding, enabling nuanced affective touch perception—a\ncapability not achieved by resistive-based platforms like TENG-DARS.\n(2) Limited Materials and Structural Innovation\nThe core materials (Ecoflex, Ag electrodes) and the “bistage hierarchical hairy structure” are\nincremental modifications of existing designs in triboelectric tactile sensors (such as Adv. Sci. 2025,\ne10741; Nano Energy 2019, 57, 424). The manuscript fails to demonstrate fundamental advances in\nmaterial composition, structural optimization, or processing technology that distinguishes it from\nprior works.\nResponse to reviewer:\nWe sincerely thank the reviewer for raising this critical point regarding the advancement beyond\nexisting triboelectric tactile sensor designs. We acknowledge that Ecoflex and Ag electrodes are\nindeed widely used, and hairy structures have been explored in the literature. However, our work\nintroduces fundamental advances not by reinventing these core components, but through a novel\napplication paradigm targeting affective touch perception and significant processing\ntechnology innovations that enable this new functionality. Below, we provide a detailed\nclarification.\n1. Innovation in application paradigm: from discriminative to affective touch\nIt is true that hairy structures have been employed in tactile sensing, primarily for detecting physical\nquantities like force, vibration, or airflow (discriminative touch). The fundamental advance of our\nwork lies in leveraging this biomimetic structure specifically for affective touch perception, a\nvastly underexplored area. Biological hairy skin, richly innervated by C-LTMRs, is the primary\npathway for encoding pleasant touch. Our bistage hierarchical hairy structure is therefore not a mere\nincremental modification but a deliberate design choice to mimic this affective biological pathway.\nThis allows our sensor (BioAI2) to replicate neurophysiologically relevant response curves, such as\nthe inverted U-shaped relationship between stroking velocity and firing frequency—a hallmark of\naffective touch that had not been reliably achieved in artificial sensors before. Thus, the novelty is\nin the functional objective and the resulting biomimetic encoding capability, enabling the robot\nto distinguish between, for instance, a comforting stroke and a forceful push based on their\nemotional resonance.\n2. Fundamental advance in processing technology: electrospinning for unprecedented\nuniformity\nA key technological advancement in our work is the electrospinning process used to fabricate the\nconductive PVC-DBA nanomesh film. While solution-based methods for conductive gels are\ncommon, they suffer from inherent limitations like the Marangoni effect and coffee-ring effect,\nleading to inconsistent film thickness and conductivity, which severely compromise the accuracy of\nspatial signal acquisition (e.g., for trajectory tracking).\nOur electrospinning technique represents a significant processing breakthrough. As detailed in Fig.\nR2 and R3 (also in Supplementary Figures 15 and 16), this method enables the creation of a large-\narea, highly uniform nanomesh film. Characterization via SEM and AFM confirmed a maximum\nheight variation of less than 1.5 µm and an average roughness (Ra) of 150 nm over a large area (10\ncm × 10 cm). The resulting continuous and stable isoline (Fig. R2 b(ii)) directly translates to superior\npositioning accuracy, forming the foundation for reliable tactile feature extraction. This\nnanotextured interface engineering reconciles the conflicting demands of high uniformity and large-\narea fabrication, going beyond prior works.\n3. Material integration for a novel system-level function\nThe combination of the optimized electrospun nanomesh with the hierarchical hairy structure and a\nsophisticated neural network-based decoding algorithm creates a system capable of closed-loop\nnatural emotional interaction. This integration allows for real-time interpretation of tactile\ngestures in context, a functionality not demonstrated in the referenced prior works focused on\ndiscriminative touch. Therefore, the core distinction of BioAI2 lies in this system-level innovation\nthat enables nuanced human-robot emotional communication, moving beyond basic physical\nparameter detection.\nFig. R2 Illustration of the positioning ability of the BioAI2. a (i) Illustration of volatilization of\na liquid film. During the evaporation process, due to the influence of external conditions such as\nhumidity and airflow, there exist differences in evaporation rates across various regions of the liquid\nsurface. This leads to variations in temperature and concentration within different areas of the liquid,\nresulting in discrepancies in surface tension. Such discrepancies trigger the Marangoni effect, which\ncauses uneven material flow and subsequently results in uneven thickness in the cured film.\nAdditionally, the coffee-ring effect is also present at the edge of the liquid surface, causing the film\nthickness at the edges to be thicker. These effects collectively contribute to the significant thickness\ninhomogeneity observed in the solidified film. (ii) The measured isoline distribution of the\nconductive layer fabricated by volatilization of a liquid film. b (i) Illustration of a conductive layer\nby electrostatic spinning. The SEM images are shown in Supplementary Fig. 16. (ii) The measured\nisoline distribution of the conductive layer fabricated by electrostatic spinning. c Illustration of the\ntriboelectric signals and corresponding time-frequency diagram generated from (i) a traditional\nplane interface and (ii) the proposed BioAI2. d Comparison with other excellent works on the cutting\nedge2,8,9,12-14,21-24.\nFig. R3 | Surface topography characterization of the fabricated conductive layer. a Photograph\nof the conductive layer. b Scanning electron microscope image (SEM) of the conductive layer. c\nEnlarge view of the image in b. d SEM image of the conductive layer in another part of the\nmembrane. e Enlarge view of the image in d. (Scale bars: 1 cm (a), 5 μm (b), 500 nm (c), 5 μm (d),\n500 nm (e).) The thickness and distribution of fibers in (b,c) and (d,e) are similar which indicates\nthat the fabricated conductive layer is relatively uniform. f-h Atomic force microscope image (AFM)\nof the conductive layer. R = 150 nm. (Scale bars: 2 μm (f), 6 μm (g)). The AFM reveals that the\na\ndifference in height of the conductive layer is lower than 1.5 μm.\n（3）Unquantified Fusion Mechanism Between CNN and LLM for Emotional Interaction\nThe manuscript claims to integrate a ResNet-based CNN (for emotion recognition, 82.37% accuracy)\nwith ChatGPT (for contextual emotional understanding) to form a closed-loop interaction. However,\nit fails to clarify how tactile features extracted by the CNN (e.g., spike frequency, force magnitude)\nare quantitatively mapped to ChatGPT’s input, and how conflicts between \"tactile emotion cues\"\nand “contextual information” (e.g., a “gentle stroke” but negative scenario context) are resolved.\nThe fusion process remains a “black box”, lacking experimental validation of the contribution of\neach component.\nResponse to reviewer:\nFig. R3 Comparison of affective touch perception process between human brain and the BioAI2.\n1. Fusion mechanism between CNN and LLM\nIn humans, the research by LeDoux and colleagues highlights two pathways underlying emotional\nprocessing25. The first pathway, which operates without involvement of the cerebral cortex, is the direct\nthalamo-amygdala route. This pathway can process crude sensory aspects of incoming stimuli and\ndirectly relay this information to the amygdala, thereby generating an early conditioned emotional\nresponse. This echoes psychological ideas about emotion activation, notably Zajonc's position regarding\nemotions without cognition. The second pathway is the thalamo-cortico-amygdala pathway. With the\nparticipation of the cerebral cortex, it allows for a more complex analysis of the incoming stimulus and\ndelivers a slower, conditioned emotional response, thereby enabling emotional regulation and analysis\nin complex environments.\nInspired by LeDoux’s dual-pathway model of emotion processing in humans, our system mimics two\ncomplementary pathways (Fig. R3):\n Fast pathway (BioAI2-CNN): Analogous to the thalamo-amygdala route, this pathway\nprocesses low-level tactile features (e.g., spike frequency, force magnitude) from the spike\noutput of BioAI2 rapidly and automatically. The CNN extracts spatiotemporal patterns and\nclassifies emotions into coarse categories (positive/neutral/negative) and gesture types\n(stroke/pat/etc.) with an overall accuracy of 82.37%. This stage operates without contextual\nreasoning, similar to primal emotional reactions.\n Slow pathway (CNN-ChatGPT): Mirroring the thalamo-cortico-amygdala pathway, ChatGPT\nincorporates contextual information (scenario, user/dog profiles, relationship) to refine the\ncoarse emotional labels from the CNN. This enables nuanced interpretation through advanced\nreasoning, aligning with higher-order cognitive processes.\nSpecifically, the CNN analyzes the input pulse signals to output preliminary emotion labels (positive,\nneutral, negative) and gesture recognition results. As ChatGPT models cannot directly process raw tactile\nfeatures, the detailed tactile features extracted by the CNN (e.g., spike frequency, force magnitude) are\nnot fed directly into ChatGPT. Instead, we provide ChatGPT with the preliminary emotion labels and\ngesture labels — semantic information that the LLM can readily understand. ChatGPT then\nprocesses this information in conjunction with the given context to further identify more specific\nemotional categories. In this way, leveraging its zero-shot capabilities, ChatGPT can achieve relatively\naccurate emotion analysis and expression results without requiring task-specific training.\nThe specific interaction process with ChatGPT is as follows:\n[CONTEXTUAL INFORMATION] Imagine a scenario where there is a man who is lively,\ncheerful and loves life. He has a dog. One morning, the man is going to work.\n[AFFECTIVE TACTILE INPUT] The man strokes the dog and his mood is positive.\n[USER QUERY 1] I need you to play as the dog to further analyze the owner's mood, summarize it\nin one word, and explain the reason.\n[CHATGPT OUTPUT 1] The owner’s mood is joyful. This is evident through their lively and\ncheerful demeanor, as well as their love for life and positive attitude. Stroking her dog before leaving\nfor work indicates a sense of happiness and contentment in their daily routine.\n[USER QUERY 2] How do you respond?\n[CHATGPT OUTPUT 2] I will wag my tail and sniff your hand to show my curiosity and excitement.\nThen I will jump up and down, nodding my head to convey my joy and happiness. Finally, I will lean\nagainst you and lick your face, expressing my closeness and love for you.\n2. Conflicts between “tactile emotion cues” and “contextual information”\nFirst, it is important to emphasize that the relationship between a physical gesture and the emotion it\nconveys is not a simple one-to-one mapping. The meaning of a specific gesture, such as a “stroke”, is\nhighly dependent on the dynamics of its execution—including the pressure, speed, duration—and,\ncrucially, the interaction scenario in which it occurs26. For instance, a gentle stroke in a moment of\nsadness may express comfort and reassurance, while a lively, rapid stroke in a joyful context might\nconvey affection and playfulness. Therefore, the scenario mentioned by the reviewer—a “gentle stroke”\nwithin a “negative scenario context”—is not necessarily a contradiction. Instead, it can represent a\ncomplex interaction where the touch serves to seek comfort or provide support amidst a negative\nemotional state, a phenomenon commonly observed in natural human communication. Our system is\nspecifically designed to capture this inherent complexity in affective touch. Below is the experimental\nvalidation of such scenario.\n3. Experimental validation of the contribution of each component.\nTo analyze the contribution of contextual information and tactile emotion cues to ChatGPT’s reasoning,\nwe conducted a comparative experiment. The scenario was set as the owner returning home from work.\nThe contextual information provided was: “Imagine a scenario where there is a man who is lively,\ncheerful and loves life. He has a dog. One evening, the man comes home from work.” The tactile emotion\ncues given was: “The man strokes the dog and his mood is negative.” Under three conditions—\ncontextual information only, tactile emotion cues only, and both contextual information and tactile\nemotion cues—we asked ChatGPT to quantify the likelihood of the owner being in different emotional\nstates. The corresponding results are as follows:\nExperiment 1: Contextual information only\n[CONTEXTUAL INFORMATION] Imagine a scenario where there is a man who is lively,\ncheerful and loves life. He has a dog. One evening, the man comes home from work.\n[AFFECTIVE TACTILE INPUT] None.\n[USER QUERY 1] I need you to play as the dog to further analyze the owner's mood. Please quantify\nthe possibilities of the owner under different emotions.\n[CHATGPT OUTPUT]\n Positive Mood – Happy / Relieved / Excited (Probability: 55%)\nInterpretation: Work went well, maybe got praised or finished something important. Ready to relax\nbut energized by being home.\n Neutral Mood – Tired / Content / Calm (Probability: 30%)\nInterpretation: A regular day. Nothing special happened. Physically tired but emotionally neutral.\nLooking forward to rest and maybe some quiet time together.\n Negative Mood – Stressed / Sad / Frustrated (Probability: 15%)\nInterpretation: Something didn’t go right today. Could be work stress, personal issue, or just general\nfatigue. Needs space or comfort.\nExperiment 2: Tactile emotion cues only\n[CONTEXTUAL INFORMATION] None.\n[AFFECTIVE TACTILE INPUT] The man strokes the dog and his mood is negative.\n[USER QUERY 1] I need you to play as the dog to further analyze the owner's mood. Please quantify\nthe possibilities of the owner under different emotions\n[CHATGPT OUTPUT]\n Sadness (Probability: 40%)\nInterpretation: The man may have had a loss, bad news, or is just having a hard day. My presence\nseems comforting, but he’s not perking up.\n Stress/Anxiety (Probability: 35%)\nInterpretation: Something is on his mind—work pressure, family issues, or general worry. He seeks\ncalm through me, but it’s not fully working.\n Guilt or Self-Blame (Probability: 10%)\nInterpretation: Maybe he feels like he’s not being a good owner right now, or something happened\nthat he thinks he could’ve prevented.\n Anger (Probability: 10%)\nInterpretation: If he were truly angry, I’d feel it in his grip. It’s possible he’s mad at someone else\nor about a situation, but he’s redirecting that emotion by seeking comfort in routine.\n Depression (Probability: 5%)\nInterpretation: This is rarer, but if it’s been going on for days, I’d be concerned. He may need more\nthan my company to recover.\nExperiment 3: contextual information and tactile emotion cues\n[CONTEXTUAL INFORMATION] Imagine a scenario where there is a man who is lively,\ncheerful and loves life. He has a dog. One evening, the man comes home from work.\n[AFFECTIVE TACTILE INPUT] The man strokes the dog and his mood is negative.\n[USER QUERY 1] I need you to play as the dog to further analyze the owner's mood. Please quantify\nthe possibilities of the owner under different emotions\n[CHATGPT OUTPUT]\n Tired + Frustrated (Probability: 45%)\nInterpretation: Work probably didn’t go great. Deadlines, meetings, maybe someone took credit for\nyour idea. You're holding it together, but you're worn out and a bit frustrated.\n Worried or Anxious About Something (Probability: 25%)\nInterpretation: Something is on his mind—work pressure, family issues, or general worry. He seeks\ncalm through me, but it’s not fully working.\n Disappointed or Discouraged (Probability: 15%)\nInterpretation: Maybe someone criticized you unfairly. Maybe things didn’t go your way despite\neffort. Whatever it was, it hit you harder than you show.\n Irritated / Annoyed (Probability: 10%)\nInterpretation: You're annoyed—probably not at me (thank goodness)—but at something or someone\nwho got under your skin. Could be traffic. Could be Karen from accounting again.\n Feeling Blue / Emotionally Drained (Probability: 5%)\nInterpretation: Today wasn’t just bad—it drained you emotionally. Maybe you smiled all day for\nothers, but now you’re alone, and the mask is off.\nFrom the results, in the contextual information-only condition, ChatGPT could not determine whether\nthe person’s emotion was positive, neutral, or negative, though it still showed a bias toward positive\nemotion (probability up to 55%) based on the owner’s generally cheerful personality. In the tactile\nemotion cues-only condition, ChatGPT’s judgment clearly leaned toward negative emotion, yet this\nnegative emotion could be classified into many possible categories (e.g., Sadness 40%, Stress/Anxiety\n35%). Without scenario information, ChatGPT could not specify which category was most plausible. In\nthe combined condition (both contextual information and tactile emotion cues), ChatGPT’s judgment\nstill exhibited a clear negative emotional tendency, but the classification became more concentrated\n(Tired + Frustrated 45%, other emotions ≤ 25%). This demonstrates that having both contextual\ninformation and tactile emotion cues helps ChatGPT more confidently determine the owner’s precise\nemotional category.\nThe results clearly demonstrate that:\n Tactile emotion cues primarily determine the basic emotional valence (positive, neutral,\nnegative). In the fused condition, the output correctly aligns with the negative label provided by\nthe CNN.\n Contextual information is crucial for refining the coarse emotion into a specific, nuanced\ncategory. It helps disambiguate the reason behind the tactile cue by integrating scenario\nknowledge.\n The fusion of both components yields a more confident, precise, and contextually appropriate\nemotional interpretation than either modality alone. This aligns with the earlier explanation:\nChatGPT incorporates contextual information (scenario, user/dog profiles, relationship) to\nrefine the coarse emotional labels from the CNN. This enables nuanced interpretation through\nadvanced reasoning, aligning with higher-order cognitive processes.\nMeanwhile, the corresponding modification has been highlighted in our revised manuscript. Specifically,\nthe details are shown below and in Supplementary Note 8:\nFrom page 24, line 38 to 41: “Meanwhile, through comparative experiment validation in Supplementary\nNote 8, both of the contextual information and tactile emotion cues are crucial for ChatGPT to yield a\nmore confident, precise, and contextually appropriate emotional interpretation than either modality alone.”"}]

Decision letter