A liquid metal dynamic wetting strategy for spatiotemporal monitoring of hand movements

Domain: physics
Published: 2026-01-06
Editorial outcome: major_revision

Abstract
Current hand tracking technologies often suffer from inconsistent accuracy due to occlusion, electromagnetic interference, and ambiguous signals from soft sensors. These limitations hinder reliable motion capture for advanced human-machine interaction. To overcome them, we propose a liquid metal dynamic wetting-based flexible and wearable inertial skeletal tracking (FWIST) system. The system integrates 16 FWIST units on the hand to capture palm pitch/roll and finger bending angles in real time with high precision. It accurately reconstructs fingertip and joint trajectories, providing comprehensive hand motion analysis. In laboratory tests, a feedforward neural network achieved nearly 100% gesture recognition accuracy on a small dataset, proving the method’s potential. Future work will focus on verifying robustness on larger, diverse datasets. The FWIST approach offers promising applications in teleoperation, robot programming, drone control, and immersive VR/AR experiences.

Introduction
The flexibility and multifunctionality of the human hand are distinguishing features that set it apart from the hands of animals, enabling it to perform a wide range of tasks, from simple grasping to complex sign language communication1–4. As a result, real-time tracking and reconstruction of hand movements have long been a popular research topic and have found extensive applications in fields such as robotics5, healthcare6, gaming7, as well as virtual and augmented reality8. Researchers have conducted extensive studies on hand pose tracking and reconstruction9–13. Nevertheless, the inherently complex structure of the hand, its numerous degrees of freedom, and the considerable anatomical variations among individuals presents significant challenges for accurate hand motion estimation. Consequently, the majority of existing studies have been confined to limited scenarios and have not been able to fully capture and assess the entire spectrum of hand movements.

To address the issue of hand tracking, three primary approaches have been proposed. Firstly, vision-based hand tracking utilizes cameras to achieve markerless hand motion tracking14–21, often relying on machine learning techniques trained on large image datasets for processing22–24. While machine learning demonstrates strong generalization in handling unseen hand shapes, object interactions, and varying lighting conditions, vision-based methods remain fundamentally constrained by occlusion issues25. Secondly, IMU-/compass-based wearable hand tracking systems typically use six-axis IMUs (combining accelerometers and gyroscopes) and magnetic compasses, which are mounted on each bone of the hand to measure three-axis orientation data26–30. When combined with additional hand position sensors, these systems can reconstruct hand movement31–37. However, this approach has inherent limitations, such as susceptibility to magnetic field variations or interference. Furthermore, these sensing units are often rigid and bulky, negatively impacting wearability and user comfort, thereby limiting their practicality and user adoption38,39. Finally, flexible wearable hand tracking systems leverage multiple flexible sensors to estimate hand postures, generating signals in response to deformation. These sensors are attached to the hand and, when combined with additional posture sensing technologies, can optimize hand tracking40–45. However, these systems also have inherent drawbacks. Firstly, they struggle to distinguish between deformations caused by hand movements and those resulting from contact with objects. As a result, their performance becomes inadequate for scenarios where users manipulate objects, use tools, or wear tactile devices. Additionally, calibration challenges arise due to sensor hysteresis and coupling effects between sensors. Their durability is also limited, as the sensors are prone to damage under large deformations or repeated bending. These issues significantly hinder the reliability and practicality of this approach in real-world applications.

Enhancing methodologies for hand motion analysis is crucial to achieving more comprehensive and adaptable results. To tackle existing limitations, we propose a liquid metal (LM) dynamic wetting triggered FWIST sensing strategy. Our approach involves creating a flexible, wearable, miniature FWIST unit by injecting LM, which possesses excellent conductivity and fluidic properties, into microchannels with scale-like microstructured inner walls. These specially structured microchannels are achieved by replicating the morphology of horsehair surfaces (Supplementary Figs. 1–5). Significantly, unlike traditional wearable sensors that are typically placed at joint locations, our FWIST unit is strategically mounted at bone positions (Fig. 1a). This innovative design results in a solid (PDMS, polydimethylsiloxane)-liquid (LM)-gas (air) three-phase interface, characterized by a pressure gradient. The dynamic additional pressure associated with the tilt angle induces a shift in the gas-liquid interface, which in turn alters the surface morphology of the LM. This change enables the acquisition of tilt angle data through precise resistance measurements. (Fig. 1b). In practical applications, the FWIST on the surface of the bone can provide pitch and roll angle information, which can be used to reconstruct the hand model during the processes of pitching, rolling, and grasping (Fig. 1a). This proposed hand tracking strategy offers accurate, robust, and cost-effective spatiotemporal monitoring of hand motions, addressing the fundamental limitations of existing methods. Key advantages of our FWIST sensing strategy include its independence from visual occlusions and complex environments; the system’s functionality is unaffected by these challenges. Additionally, the FWIST unit is designed for wearability, being fully flexible with a compact volume of 1mm³. It provides comfort and ease of wear without deformation during hand movements.

Furthermore, the system’s monitoring range and utility can be significantly enhanced through the integration of machine learning techniques, thereby increasing its versatility and potential applications (Fig. 1c). For instance, in medical rehabilitation, this approach can play a crucial role in enabling communication for individuals with speech impairments by detecting their hand movements and converting these gestures into speech. In the realm of human-computer interaction, integrating this technology directly into wearable interfaces has the potential to significantly enhance precision control capabilities within virtual reality/augmented reality systems. Furthermore, in the domain of flexible robotics, the sensing units can be strategically embedded within robotic systems in a “vascular-like” configuration, effectively functioning as both conductive pathways and posture sensors.

Results
The working mechanism of FWIST unit
We precisely controlled the pitch angle (θ) and roll angle (ω) of the FWIST unit using a dual-axis motorized rotary stage (Fig. 2a). Simultaneously, the resistance signal characteristics of a sample with a length of L and an inner diameter of D were collected under different θ and ω values using an Agilent 34420A (Fig. 2b). At this point, at a distance s from the origin, the LM deforms under the additional pressure at the three-phase interface on the inner wall of the microchannel. The variation in additional pressure P is mainly influenced by θ and ω, and can be expressed as:

P(s,ω,θ) = ρgscos(ω)sin(θ) (1)

where the g is a constant value46–50 (Supplementary Figs. 6–8). When the FWIST unit is placed on the motorized rotary stage at a fixed ω, the additional pressure P at positions gradually increases, and the larger the s value, the greater the change in additional pressure, that is, P(x, ω, θ) < P(s, ω, θ) < P(s, ω, θ). Consequently, the liquid surface of the LM becomes smoother, and the resistance signal is expected to exhibit a cosine function characteristic as θ continues to increase (Fig. 2c). We investigated the resistance response characteristics of FWIST unit as a function of θ under varying ω values. The experimental data reveal a strong correlation between the resistance signal changes and the parameters θ and ω. Specifically, as θ increases from −90° (vertical state) to 270°, the resistance change rate exhibits a sinusoidal trend. Additionally, as ω increases, the amplitude of the resistance changes rate decreases progressively, following a cosine-like relationship with respect to ω (Fig. 2d and Supplementary Video 1).

Another significant factor contributing to the observed strong correlation between the resistance signal changes and the parameters θ and ω is the tight adhesion between the LM and the PDMS substrate (Supplementary Fig. 9). This adhesion prevents the sliding of the three-phase interface during deformation. Moreover, the adhesion of the oxide layers significantly alters the wettability of the PDMS surface with respect to the LM, which in turn influences the migration dynamics of the gas-liquid interface. To explore this effect, we measured the contact angle of LM on two distinct surfaces, which are untreated PDMS and PDMS surfaces coated with the LM’s native oxide layer. On pristine PDMS, the apparent contact angle was 150.8°, indicating a strong non-wetting behavior. In contrast, the contact angle on the oxide-coated PDMS was dramatically reduced to 16.8°, reflecting a significant enhancement in wettability (Supplementary Fig. 10). These findings underscore that the oxide layer plays a critical role in modifying the interaction between PDMS and the LM, thereby influencing the migration of the gas-liquid interface, particularly at the boundary between untreated PDMS regions and those with the LM oxide coating. Specifically, the Raman spectrum of PDMS indicates the presence of Si-O-Si bonds and -CH3 groups on its surface (Fig. 2e and Supplementary Fig. 11). Meanwhile, X-ray photoelectron spectroscopy (XPS) confirms the presence of an oxide layer on the surface of LM. The Ga3d spectrum establishes the surface oxidation state of LM, where exposure to air leads to the formation of a Ga2O3 layer corresponding to the Ga(III) peaks, with a binding energy of 20.4 eV (Fig. 2f and Supplementary Fig. 12). This suggests that the adhesion between PDMS and LM primarily arises from van der Waals forces between Ga and O atoms on the LM surface and Si, O, C, and H atoms on the PDMS surface. Although these forces are fundamentally weak interactions, the oxide layer on the LM acts as a “soft solid,” allowing extensive conformal contact with the PDMS surface. Consequently, despite the weak interaction among individual atoms, the large overall contact area results in a cumulative effect that renders the van der Waals forces significantly impactful at the macroscopic level, thereby facilitating effective adhesion (Fig. 2g).

To further investigate the microchannel phenomena that are challenging to characterize and observe directly, we conducted simulations and modeling of the dynamic deformation behavior of the LM under the influence of additional pressure within the microchannel. The simulation results demonstrate that, upon injection into the microchannel, the LM does not fully occupy the microstructural valleys, instead forming a clear gradient distribution. Specifically, the metal content decreases progressively from the inlet (left) to the outlet (right) (Fig. 2h). Furthermore, during the rotation of the FWIST unit, the LM content within the microstructural valleys dynamically changes in response to variations in the angular position, θ, driven by changes in additional pressure. For example, the LM content in the microstructural valley outlined by the black box varies as θ progresses from 0° to 90°, 180°, and 270°. The corresponding LM content changes from 77.51% to 77.35%, 77.82%, and 77.52%, respectively. Similarly, the LM content at different positions within the microstructural valleys also changes as θ increases. Notably, the variation in LM content follows a sine-function-related trend with respect to θ (Fig. 2i). In addition, we simulated the resistance response characteristics of FWIST unit under varying angular velocities to examine their dependence on both θ and ω. The simulation results reveal a strong and quantifiable correlation between these two parameters and the resistance signal changes of FWIST unit. Specifically, as θ increases from −90° (vertical state) to 270°, the resistance changes rate exhibits a sinusoidal trend. Moreover, as ω increases, the amplitude of the resistance change rate gradually diminishes, demonstrating a cosine-like trend in relation to ω (Fig. 2j and Supplementary Fig. 13). These findings are in strong agreement with experimental observations, further validating the robustness of our simulations and the accuracy of the experimental setup.

The working characteristics of FWIST unit
The FWIST unit was mounted on the axle head of a spool to examine its operational mechanism by monitoring the rotational motion of the spool’s axis during the unrolling process. As the spool unfolds, the FWIST unit undergoes periodic rotational motion that is precisely synchronized with the axis’s unrolling. This rotational movement induces corresponding periodic variations in the electrical signal generated by the unit. Notably, these electrical signal variations exhibit a distinct sinusoidal pattern, which reflects the intrinsic coupling between the rotational dynamics of the FWIST unit and its electrical response. This behavior provides a clear demonstration of the FWIST unit’s functionality and its sensitivity to periodic motion, showcasing its potential utility in dynamic sensing applications (Fig. 3a). When the FWIST unit is initially set to a value of −90° and continuously rotates counterclockwise through a full 360° cycle, the generated electrical signal exhibits a well-defined sinusoidal variation (Fig. 3b). Within the operational range of −90° to 90°, the FWIST unit achieves a high resolution of 2°, and the corresponding signal variations are characterized by exceptional accuracy, stability, and the absence of drift. This consistent performance highlights the reliability of the FWIST unit for precise angular measurements under static conditions.

Beyond its excellent static testing capabilities, the FWIST unit also demonstrates remarkable dynamic detection performance, making it suitable for applications involving rapid angular transitions. When subjected to fast angular changes, the FWIST unit responds with high efficiency, being capable of generating a step response within just 0.27 s (Fig. 3c and Supplementary Video 2). This rapid response is indicative of the device’s superior sensitivity and robustness, underscoring its potential for use in dynamic sensing scenarios where speed and precision are critical. We further investigated the lag characteristics of the electrical signal variations of the FWIST unit during continuous rotation, which arise due to the inherent response time of the device. As the rotational frequency of the FWIST unit increased from 0.034 Hz to 0.34 Hz, the amplitude of the electrical signal variation within a single rotational cycle decreased from 1.17% to 1.1% (Fig. 3d and Supplementary Fig. 14). This reduction in signal amplitude with increasing rotational frequency highlights the influence of the response time on the device’s performance at higher operating speeds. These findings provide insights into the dynamic behavior of the FWIST unit and suggest areas for potential optimization to maintain signal fidelity under rapid rotational conditions.

In contrast to traditional flexible sensors that rely on large-scale deformations, the resistance variation of our FWIST unit is primarily driven by the localized, small-scale deformation of the LM within the anisotropic microchannel structures. These controlled deformations prevent the rupture of the LM’s oxide layer, thereby ensuring signal stability during operation. Furthermore, the cumulative effects of the numerous microstructures amplify the signal magnitude without introducing drift, as demonstrated by the sensor’s stable performance over more than 5000 cycles of testing (Fig. 3e and Supplementary Fig. 15). In addition to its excellent cyclic stability, the FWIST unit exhibits remarkable long-term reliability, maintaining consistent performance over a duration of 30 days (Fig. 3f). These findings underscore the FWIST unit’s robustness and highlight its potential for applications requiring high durability and stability under both dynamic and long-term operational conditions.

Furthermore, we performed a systematic analysis of the factors influencing the amplitude of electrical signal variations, specifically focusing on the LM injection speed during sample preparation, as well as the rotational frequency and ω of the sample during operation. The experimental results reveal that both the injection speed during fabrication and the ω have a pronounced effect on the signal amplitude. Importantly, the observed trends in the experimental data closely align with the results of simulation studies, thereby validating the experimental findings and confirming the robustness of the predictive model (Fig. 3g, h and Supplementary Figs. 16–20). This alignment between experimental and simulation data underscores the critical role of precise control over both fabrication parameters and operational conditions in optimizing the performance of the FWIST unit. By understanding and fine-tuning these factors, it is possible to enhance the device’s performance while ensuring consistent and reliable signal output across a range of applications.

FWIST for monitoring hand movements
In daily life, the range of motion of the human hand primarily consists of three key types of movements: the pitch movement of the palm (upward and downward tilting motion), the rotational movement of the palm (pronation and supination), and the grasping or flexion-extension movements of the fingers. These motions are fundamental to performing various tasks, from simple daily activities to complex manipulations, and highlight the versatility and functional significance of the human hand in adaptive interactions with the surrounding environment. When FWIST is applied to track hand posture, a total of 16 FWIST units are required to work in coordination. Among them, two FWIST units (unit-1 and unit-2) are configured vertically on the back of the hand to track overall hand movement, while the remaining 14 units (unit-3 to unit-16) are distributed along the phalanges of each finger, oriented axially along the length of the fingers. Specifically, FWIST unit-1 is responsible for monitoring the overall pitch angle (θ) of the hand, while FWIST unit-2 is used to track the overall roll angle (ω) of the hand. The other 14 FWIST units are utilized to monitor the bending angles (θ) of the individual phalanges, providing detailed information on the flexion and extension of each finger (Fig. 4a and Supplementary Fig. 21). And by explicitly defining the direction of the arrow to represent the injection direction of the LM within the channel, thus clarifying the orientation of the FWIST on the finger. Specifically, when the arrow points vertically downward, we define the tilt angle as 0°, when the arrow points vertically upward, the tilt angle is defined as 180°. This configuration enables comprehensive tracking of hand pose and motion, providing high-resolution data for applications such as gesture recognition, biomechanical analysis, and human-machine interaction. It also offers robustness against sweat, dirt, and grime, ambient temperature variations, and repeated mounting and removal (Supplementary Figs. 22–24).

The range of pitch motion for the human palm typically spans from −90° to 90°. Within this range, there exists a clear one-to-one correspondence between the resistance signal variation rate of FWIST unit-1 and the tilt angle, allowing for accurate identification of the palm’s pitch posture using FWIST unit-1 alone (Fig. 4b). Similarly, the range of roll motion for the human palm is −90° to 180°. In the range of −90° to 90°, the electrical signal variation rate of FWIST unit-2 also exhibits a one-to-one correspondence with the rotational angle, enabling precise monitoring of the palm’s roll angle. However, within the range of 90° to 180°, the electrical signal variation overlaps with that observed in the 0° to 90° range, making it challenging to unambiguously determine the roll angle using FWIST unit-2 alone (Fig. 4c). To address this limitation, FWIST unit-8 can be used to assist in achieving precise identification of the roll angle. Specifically, in the rotational range of 0° to 90°, the electrical signal of FWIST unit-8 exhibits an increasing trend, whereas in the range of 90° to 180°, the electrical signal exhibits a decreasing trend. This behavior is influenced by the structural characteristics and bending direction of the human fingers, which affect the measurements from FWIST unit-8. Once the pitch and roll angles of the palm are determined through these combined measurements, it becomes straightforward to monitor the bending states of the fingers and further track the current spatial posture of the entire hand.

We illustrate this by tracking gripping actions when the palm is in eight different postures using FWIST units 1, 2, 8, 9, and 10 (ω=0°, θ=0°, 45°, 135°, 180°; θ=90°, ω=0°, 45°, 135°, 180°). When ω=0° and θ=0°, 45°, 135°, 180°, the roll and pitch angles exhibit a one-to-one correspondence with the rate of change in the electrical signal, highlighting a direct relationship that underscores their interdependence. Consequently, the hand gesture posture can be easily identified (Fig. 4d–g). Similarly, when θ=90° and ω=0°, 45°, 135°, 180°, the pitch angle of the hand posture can be directly determined using FWIST unit-1. While the electrical signal variation rates are identical at ω=45° and ω=135°, FWIST unit-8 distinguishes between these by showing a gradually increasing electrical signal variation rate at ω=45° and a gradually decreasing rate at ω=135° (Fig. 4h–k). These distinct trends indicate that precise hand posture recognition can be achieved by simultaneously utilizing multiple FWIST units, demonstrating the system’s capability for detailed spatial posture tracking. And this capability highlights the effectiveness of the FWIST system for accurately capturing complex hand postures, making it valuable for applications such as gesture recognition, biomechanics analysis, and human-computer interaction.

FWIST for spatiotemporal monitoring of hand movements
We equipped the hand with the FWIST system and sequentially executed actions 1 through 8 to thoroughly evaluate its performance. The system comprises 16 FWIST units working collaboratively to capture comprehensive posture information of the hand (Fig. 5a). Specifically, FWIST unit-1 is responsible for measuring the hand’s pitch angle, providing insights into its vertical orientation, while FWIST unit-2 records the roll angle, essential for understanding lateral movements. The remaining units are meticulously dedicated to capturing the bending angles of each finger bone, offering detailed analysis of finger joint dynamics (Fig. 5b). This configuration enables precise monitoring and analysis of complex hand movements for various applications.

Furthermore, leveraging the capabilities of MATLAB, we developed a comprehensive 3D model of the hand (Fig. 5c). We meticulously imported the time-dependent angular data acquired by each FWIST unit into the MATLAB environment to accurately simulate and control the hand model’s dynamic movements. The angular measurements from FWIST unit-1 and unit-2 were strategically utilized to govern the overall hand’s pitch and roll angles, respectively, offering critical insight into the hand’s orientation in three-dimensional space. In contrast, the remaining 14 FWIST units were exclusively tasked with regulating the precise bending angles of each individual finger bone, thus providing a detailed depiction of finger articulation. In our analysis, we paid particular attention to the intricate movement patterns of the middle finger, specifically tracking the trajectories of the joint-1, joint-2, and fingertip as the hand performed sequential actions 1 through 8 (Fig. 5d, e and Supplementary Video 3).

We generated projections of these trajectories on the X–Y, X–Z, and Y–Z planes to visualize and analyze the spatial dynamics. We observed that certain actions yielded identical motion paths for the middle finger, prompting us to selectively illustrate the trajectories for actions I, III, IV, V, and VI in the projection planes for clarity (Fig. 5f). The results of these trajectory analyses underscore the efficacy of our FWIST system, demonstrating its robust capability to capture and analyze the complex spatiotemporal characteristics of hand movements in a detailed and reliable manner. We also developed an advanced feedforward neural network designed with two hidden layers to analyze hand movement data. The architecture of the network includes 20 neurons in the first hidden layer and 15 neurons in the second hidden layer, optimized to capture the nuances of hand postures. We systematically collected electrical signals for hand actions I-VIII (50 trials per action), yielding a relatively small dataset of 400 samples with limited diversity. After random shuffling to mitigate bias, the data were split into training, validation, and test sets (70%, 15%, and 15%, respectively). On this dataset, our machine-learning model achieved 100% accuracy in classifying the eight hand postures, but these results remain preliminary and are confined to the current, small-scale data. While the findings suggest that our neural-network architecture can capture complex, multi-degree-of-freedom hand dynamics (Fig. 5g and Supplementary Figs. 25–28), further validation on larger and more varied datasets is required to confirm robustness and generalizability. These initial outcomes indicate that FWIST may serve as a valuable tool for precise hand-motion monitoring, with future work aimed at broader experimental evaluation and real-world application.

Methods
Preparation of FWIST unit
First, we utilized PDMS silicone to replicate the surface morphology of horsehair, maintaining a mass ratio of 10:1 between PDMS and the curing agent. After immersing the horsehair in PDMS, it was placed in a vacuum oven and subjected to -0.08 MPa for 0.5 h. The pressure was then restored to atmospheric levels, and the assembly was heated at 65°C for 2 h to ensure complete curing. Subsequently, the horsehair was removed, yielding microchannels with a scale-like inner wall morphology.

The FWIST unit was fabricated by directly injecting LM into these microchannels at a speed of 1 ms−1, with the injection rate controlled by a peristaltic pump. Once the LM was injected into the microchannels, the ends of the channels were sealed with conductive silver paste, and wires were connected to form electrodes. Finally, the device was placed in a vacuum oven at 65 oC for 1 h to sinter and cure the conductive silver paste.

Record of electrical signals
The electrical signals generated during the rotational movement of the sample were meticulously measured using a high-precision digital multimeter (Agilent 34420A, USA). Meanwhile, the data were collected using the accompanying software of the device, BenchVue Basic Digital Multimeter App (2019 Update 4.0).

Raman analysis of PDMS
The Si-O-Si and -CH3 groups on the surface of PDMS were determined by confocal Raman microscope (inVia-Qontor).

XPS analysis of LM
The Ga2O3 of LM surface was determined by X-Ray Photoelectron Spectroscopy (ESCALAB 250Xi).

SEM characterization
The sample was mounted on the stage with conductive adhesive and gold-sputtered for 60 s prior to testing. Imaging was performed on a Hitachi SU8600 field-emission scanning electron microscope in secondary-electron mode at an accelerating voltage of 3 kV.

Simulation analysis
Numerical theory simulations were performed using the commercial software COMSOL Multiphysics (Version 5.6, COMSOL Inc., Sweden). All computational models have been analyzed for mesh independence, and a medium number of meshes has been used after comprehensive consideration.

Statistics and reproducibility
No statistical method was used to predetermine the sample size. No data were excluded from the analyses. The experiments were not randomized. The investigators were not blinded to allocation during experiments and outcome assessment.

Ethics declaration
The study protocol was reviewed and approved by the Institutional Human Study Ethics Committee of Beijing Forestry University (BJFUPSY-2025-075), which is affiliated with Beijing Forestry University (Beijing, China) and conducted in full compliance with relevant ethical guidelines. All participants provided written informed consent before taking part in the experiments. Written consent for the publication of any images or data that could potentially identify participants, including the main figures, Supplementary Figs. and the schematics in our point-by-point response to reviewers were also obtained.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript “A liquid metal dynamic wetting strategy for spatiotemporal monitoring of multi-degree-of-freedom hand movements” by Zhan et al., introduces the FWIST system, a flexible and wearable hand movement tracking technology designed to overcome limitations of existing methods, such as occlusions and interference. It integrates 16 units to track hand and finger movements with high accuracy, achieving 100% recognition of diverse gestures. The system is durable, with impressive stability, fast response times, and long-lasting performance.\n\nThe numerical simulation results presented in the paper effectively correlate with the experimental findings. With the support of AI, the recognition outcomes are highly accurate. However, a significant concern is the absence of key experimental results in the manuscript, as well as limited reproducibility with previous studies (Adv. Mater. 2024, 36, 2409182).\nI regret to say that I fail to see good reasons to recommend this manuscript to Nature Communications.\nI outline below my concerns regarding the results and discussions presented by the authors:\n(1) What does the microstructure of horsehair look like? What is the smallest scale of the structure? How does the PDMS channel appear after reprinting? Is there any damage to the horsehair microstructure during demolding? If so, how did the authors address this issue? The authors should provide optical and SEM images to support their explanation.\n(2) Building on the previous question, what is the smallest diameter of the PDMS microchannel? Despite the presence of an oxide layer, research has shown that surface tension of gallium-based liquid metal does not become zero in this case. Under pressure, the liquid metal injected into the PDMS channel tends to flow along the main channel, making it difficult to enter the microchannels. The authors should provide a more detailed description of the fabrication process and demonstrate the filling of liquid metal into the microchannels.\n(3) According to the theoretical analysis in the manuscript, the pressure changes when the hand is tilted is attributed to a change in gravity. While the author does not explicitly state this, the term \"(cid:1)(cid:2)\" in the formula suggests this explanation. However, at such a small scale, it remains unclear whether gravity can overcome surface tension and exert a noticeable effect. Additionally, regarding the meniscus, it appears that the author has overlooked the impact of the Laplace pressure difference.\n(4) As shown in Fig. 1b, the microchannel distribution is three-dimensional and cannot be simply represented as two upper and lower rows, as depicted in the simulation. The resulting pressure variations are also more complex. The author should provide a clear rationale for simplifying the system into a two-dimensional model.\n(5) “as demonstrated by the sensor's stable performance over more than 5,000 cycles of testing (Fig. 3e, Fig. S7).” Figure S7 presents simulation results and is unrelated to the life test.\n(6) In Figure 4, the authors should include photographs of the sensor experiments conducted with hands, rather than relying solely on schematic diagrams.", "reviewer_2": "This manuscript presents a wearable sensing strategy for monitoring multi-degree-of-freedom hand motion trajectories by placing sensing devices on non-joint regions of the hand to track angular and postural information. The proposed sensors feature a wide operational range, high stability, high resolution, fast response speed, and a compact form factor. The miniaturized design, in particular, aligns well with current trends in developing high-performance wearable sensors. Additionally, the work uniquely utilizes the wetting behavior of liquid metal to realize the sensing mechanism which shows a novel and promising application in the context of liquid metal-based sensors. The experimental data, complemented by finite element simulations, consistently demonstrate a strong correlation between the electrical signal output and inclination angle variations, thus confirming the effectiveness of the sensing concept. Overall, given the scientific merit, technical novelty, and thorough validation presented in the manuscript, I recommend acceptance for publication in Nature Communications after the authors address the following points.\n1. I suggest the authors further discuss the potential scalability of their structure. Specifically, is there a possibility to further reduce the structural dimensions? What are the key factors that might limit the minimum achievable feature size, such as the surface tension of the liquid metal, fabrication precision, or other considerations? If further miniaturization can be realized, this sensing strategy could have significant implications for applications in microelectronics or micro-electromechanical systems (MEMS). Therefore, I suggest the authors include a discussion in the revised manuscript regarding the scalability limits and possible future directions for miniaturization, to strengthen the depth and forward-looking perspective of the work.\n2. Considering that horse hair was successfully employed as a template in this work, a natural question arises as to whether the hairs from other animals could achieve similar results. Could the authors clarify why horse hair was specifically selected for fabricating the rough microtube? Were there particular features, such as hair diameter, surface roughness, mechanical properties, or availability, that made horse hair more suitable than other biological templates?\n3. In the manuscript, the authors attribute the electrical signal response to tilt angle variations to the migration of the gas-liquid interface within the microchannel. However, considering the known properties of liquid metals, the adhesion of the oxide layer to PDMS surfaces notably enhances wettability compared to pristine PDMS. This difference could significantly affect the migration behavior and thereby influence sensing performance. Although this is a fundamental phenomenon in liquid metal research, Nature Communications serves a wide interdisciplinary readership. To improve clarity, I suggest that the authors discuss the wettability differences of liquid metal on two different surfaces, namely, the bare PDMS surface and the PDMS surface with adhered liquid metal oxide layers, and provide corresponding contact angle data.\n4. In the manuscript, the electrical signal responses of the sensor vary when the two ends of the device are oriented in different directions. Therefore, in the hand motion monitoring experiments, such as those shown in Figure 4a, it would be important not only to clearly mark the placement location of each sensor, but also to indicate the orientation (directionality) of the sensor itself. Without this information, there is a risk that readers may misinterpret the setup and the corresponding signal outputs. I suggest the authors revise the relevant figures and captions accordingly, ensuring that both the positioning and orientation of the sensors are clearly labeled. This would improve the clarity and reproducibility of the experimental description.\n5. The authors mention that the core volume of the sensor can be reduced to below 1 mm3. Could the authors elaborate on how the specific dimensional parameters were determined?\n6. The XPS peak fitting in Figure 2f appears to be not well-matched.\n7. The unlabeled peaks with different colors in fig S5 require clarification and labeling.", "reviewer_3": "Title: A liquid metal dynamic wetting strategy for spatiotemporal monitoring of multi-degree-of-freedom hand movements\nAuthors: Fei Zhan, Nan Li, Ruohan Zhan, et al.\nInstitutions: Beijing Forestry University; Chinese Academy of Sciences; General Research Institute for Nonferrous Metals\n1. Summary and Context\nThis paper presents an innovative hand-tracking technology named FWIST (Flexible and Wearable Inertial Skeletal Tracking), which leverages liquid metal (LM) dynamic wetting inside microstructured PDMS channels. The authors implement 16 ultra-compact sensors per hand to monitor pitch, roll, and phalangeal joint angles in real time. The system addresses key limitations of existing methods in visual, inertial, and soft sensor-based hand-tracking platforms, particularly in contexts requiring accuracy, durability, and wearability.\nThe proposed FWIST system stands out for its minimal interference with natural hand motion, independence from environmental occlusion, and integration potential with machine learning-based gesture recognition. This work is situated at the intersection of soft electronics, human-computer interaction, and wearable sensing.\n2. Strengths\nInnovation and Technical Merit\n• Introduction of a novel LM-based resistive sensing principle utilizing three-phase pressure gradients in horsehair-replicated microchannels.\n• Demonstrated 2° angular resolution, 0.27 s response time, and high durability (>5000 cycles and 30 days of operation).\n• Effective use of dynamic wetting behavior to achieve passive angle sensing without external field interference.\nSWaP-C Compliance\n• Size and weight are minimized (sensor volume: 1 mm³); suitable for seamless wearability.\n• Passive resistive sensing requires minimal power, with only simple electronics for signal acquisition.\n• Low-cost materials (PDMS, LM) and soft lithography-compatible fabrication techniques enable cost-effective production.\n• Operational risk is minimized due to system robustness against magnetic fields, occlusion, and mechanical coupling.\nFunctionality and Return on Innovation\n• Simultaneous tracking of full hand motion (pitch, roll, individual finger joints) with high fidelity.\n• Achieved 100% gesture recognition accuracy using a lightweight feedforward neural network trained on a modest dataset.\n• Application domains include virtual/augmented reality, human-machine interaction, medical rehabilitation, and soft robotics.\n3. Limitations and Areas for Improvement\nScalability and System Complexity\n• The requirement of 16 sensors per hand, while comprehensive, could complicate integration and limit scalability in cost-sensitive or space-constrained applications.\n• The fabrication process using horsehair molds is elegant but may pose reproducibility and throughput challenges for mass manufacturing.\nValidation and Generalization\n• Gesture recognition was validated on a small, homogeneous dataset (400 samples across 8 gestures); the 100% accuracy reported may not generalize across users or scenarios without overfitting.\n• No cross-subject or cross-session variability was explored. Larger and more diverse datasets are required to establish robustness.\nEnvironmental Durability and Long-Term Stability\n• While cyclic stability and 30-day durability are demonstrated, performance under real-world factors such as perspiration, dirt, temperature fluctuations, or repeated donning/doffing remains untested.\n• The integrity of the LM oxide layer and adhesion in long-term use across environmental extremes requires further investigation.\nSystem-Level Demonstration Gaps\n• All testing was conducted using laboratory equipment (e.g., Agilent multimeter, MATLAB). A system-level demonstration with embedded electronics (e.g., microcontroller with real-time wireless transmission and on-device ML inference) would strengthen the claim of practical deployability.\n4. Conclusion\nThis paper represents a significant advancement in wearable, high-resolution hand motion tracking. The FWIST system introduces a low-cost, compact, and power-efficient solution with excellent accuracy, robustness, and multifunctionality. It offers clear advantages over existing systems in terms of size, occlusion immunity, and power consumption, with promising application across a range of domains.\nWhile a few implementation gaps remain—particularly regarding generalization, embedded integration, and large-scale manufacturing—the technical foundation and performance metrics demonstrated in this work merit strong consideration for publication in a high-impact journal.\nRecommendation: Accept with minor revisions, subject to addressing scalability and validation in real-world contexts."}, "author_response": "We are deeply grateful to the Reviewers for the valuable feedback on our work entitled “A liquid metal dynamic wetting strategy for spatiotemporal monitoring of multi-degree-of-freedom hand movements” (Article, No. NCOMMS-25-24658A). We highly agree with all the comments and have thoroughly revised our manuscript to incorporate these insights. Our aim was to further enhance the clarity and quality of the paper. For the convenience of the reviewers, we have highlighted all major changes in red in the revised version. And a point-by-point response to each review comment is provided below in blue text.\n[...]"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have conducted a substantial number of experiments in support of their conclusions. However, several critical issues—such as the visualization of liquid metal distribution and insufficient details in SEM characterization—remain unresolved, which significantly undermines the rigor and persuasiveness of the work. Moreover, the authors have not clearly articulated the advantages of this study over their prior work (Adv. Mater. 2024, 36, 2409182). If the main contribution lies merely in substituting one material with another that possesses a similar nature-inspired microstructure, the practical significance and originality of the findings remain questionable. Therefore, I regret to say that I do not recommend this manuscript for publication in Nature Communications at its current stage.\n(1) The most significant concern lies in the limited visualization of the liquid metal within the microchannel. The authors have only provided optical images, which do not clearly reveal how the liquid metal is actually distributed. In particular, it remains unclear whether the distribution aligns with what is depicted in Figure 2C of the main text.\n(2) The manuscript lacks critical information regarding the SEM characterization. The authors should specify the instruments used and the imaging parameters. Moreover, it is unclear which region of the microchannel the SEM images represent—are they longitudinal sections along the channel direction? If feasible, SEM images of cross-sections perpendicular to the channel length would more intuitively reveal the internal microstructural valley morphology.\n(3) There is a lack of detailed explanation regarding the fabrication of the core sensing elements, particularly the sealing strategy used to prevent leakage during deformation. Supplementary Figure 3 alone is insufficient to address this concern. In addition, the curing temperature of the PDMS should be clearly stated.\n(4) In calculating the Bond number, the authors appear to have used the main channel dimensions, despite the fact that the relevant three-phase interactions occur within the microstructural valleys. This approach compromises the rigor of the analysis. A recalculation using appropriate geometric parameters is necessary. Furthermore, the specific value of H used in the Bond number calculation should be explicitly stated.", "reviewer_2": "This paper presents a novel flexible and wearable inertial skeletal tracking (FWIST) strategy triggered by liquid metal dynamic wetting.This advanced system enables the real-time capture of the palm's pitch and roll angles, alongside the bending angles of the fingers, achieving an impressive precision.\nThe authors have addressed all of the concerns I raised in the previous round of review. I am satisfied with their revisions and pleased to recommend the manuscript for publication.", "reviewer_3": "The authors have made a substantial effort to respond to the earlier round of comments, and I appreciate the seriousness with which they have taken the recommendations. The revision has improved the manuscript in clarity, rigor, and completeness, and I will evaluate it in light of my original concerns.\nOn the issue of scalability and system complexity, I had noted that the requirement of sixteen sensors per hand may complicate integration in cost-sensitive or space-constrained contexts. The authors now discuss pathways toward scalability, including transitioning from horsehair molds to precision-printed polymer masters to ensure reproducibility and throughput. While this is a reasonable plan, it remains a forward-looking statement rather than supported data. I encourage the authors to make clear in the manuscript that this is a prospective solution, not yet demonstrated, to avoid overstating the maturity of the manufacturing pathway.\nOn fabrication reproducibility, the authors have provided new SEM data comparing horsehair from different batches and have fabricated and tested ten units, showing less than ten percent variance in performance. This additional work strengthens confidence in reproducibility at the laboratory scale and addresses the concern I raised.\nOn validation and generalization, I expressed concern about the reported one hundred percent accuracy given the small and homogeneous dataset. The authors have supplemented their rebuttal with details of the dataset, training protocols, and confusion matrices, which improves transparency. However, the accuracy claim remains based on only four hundred samples from eight gestures without cross-subject or cross-session validation. This limitation should be clearly acknowledged in the manuscript, and the accuracy framed as a proof-of-concept result rather than a generalizable benchmark. Without this moderation, the claim risks misleading readers about the robustness of the approach.\nOn environmental durability and long-term stability, the authors have provided new supplementary figures on performance under sweat, dirt, temperature fluctuations, and repeated donning and doffing. These additions directly address the earlier request and add credibility to the robustness of the sensors under more realistic conditions. Longer-term tests beyond thirty days would further strengthen the claim of durability, but the new evidence is already a valuable addition.\nIn summary, the revision has improved the manuscript considerably, and the authors have made good faith efforts to respond to reviewer feedback. The work is novel and promising, and the new data on reproducibility and environmental testing are convincing. At the same time, the claim of one hundred percent accuracy remains overstated given the limitations of the dataset, and the scalability pathway remains prospective rather than demonstrated. I recommend minor revision, with the expectation that the authors temper their claims on accuracy and scalability, frame their findings more cautiously, and continue to expand validation to support the long-term impact of this promising approach."}, "author_response": "We would like to express our sincere gratitude to you for the detailed and constructive feedback on our manuscript titled \"A liquid metal dynamic wetting strategy for spatiotemporal monitoring of multi-degree-of-freedom hand movements.\" We deeply appreciate the time and effort invested in thoroughly reviewing our work and providing valuable insights that will undoubtedly contribute to improving the quality and rigor of our research.\n[...]"}, {"round": 3, "reviewer_comments": {"reviewer_1": "Although the authors present this study as a meaningful advancement beyond their earlier work, the degree of conceptual and technical progress appears somewhat limited. The core mechanism and fabrication strategy remain closely aligned with their previous report, and the distinction between the two contributions is not as clearly delineated as one might expect for a manuscript at this level. Even so, the authors have carried out a substantial and diligent revision. The added visualizations, improved SEM characterization, clearer fabrication details, and refined theoretical analysis all contribute significantly to the clarity and coherence of the presentation. The effort dedicated to addressing earlier concerns is evident, and the manuscript has been noticeably strengthened in several technical aspects.\nWhile some reservations about the novelty relative to the authors’ prior study remain, the thoroughness of the revision and the improvements to the overall presentation bring the manuscript to an acceptable standard for publication.\nI recommend acceptance of the manuscript."}, "author_response": "Thank you for your positive feedback and recommendation for the publication of our work. We sincerely appreciate the time and effort you devoted to critically evaluating our manuscript."}]

Decision letter