Title: Self-Adaptive Motion Tracking against On-body Displacement of Flexible Sensors
Venue: NeurIPS 2023
Accepted: yes
Keywords: motion tracking

flexible sensor

on-body displacement

deep learning

domain adaptation
Abstract: Flexible sensors are promising for ubiquitous sensing of human status due to their flexibility and easy integration as wearable systems. However, on-body displacement of sensors is inevitable since the device cannot be firmly worn at a fixed position across different sessions. This displacement issue causes complicated patterns and significant challenges to subsequent machine learning algorithms. Our work proposes a novel self-adaptive motion tracking network to address this challenge. Our network consists of three novel components: i) a light-weight learnable Affine Transformation layer whose parameters can be tuned to efficiently adapt to unknown displacements; ii) a Fourier-encoded LSTM network for better pattern identification; iii) a novel sequence discrepancy loss equipped with auxiliary regressors for unsupervised tuning of Affine Transformation parameters.

Self-Adaptive Motion Tracking against On-body Displacement of Flexible Sensors

Abstract

Flexible sensors are promising for ubiquitous sensing of human status due to their flexibility and easy integration as wearable systems. However, on-body displacement of sensors is inevitable since the device cannot be firmly worn at a fixed position across different sessions. This displacement issue causes complicated patterns and significant challenges to subsequent machine learning algorithms. Our work proposes a novel self-adaptive motion tracking network to address this challenge. Our network consists of three novel components: i) a light-weight learnable Affine Transformation layer whose parameters can be tuned to efficiently adapt to unknown displacements; ii) a Fourier-encoded LSTM network for better pattern identification; iii) a novel sequence discrepancy loss equipped with auxiliary regressors for unsupervised tuning of Affine Transformation parameters. Experimental results show that our method is robust across different on-body position configurations. Our dataset and code are available at: https://github.com/ZuoCX1996/SelfAdaptive-Motion-Tracking-against-On-body-Displacement-of-Flexible-Sensors.

1 Introduction

Following the philosophy of ubiquitous computing, wearable devices have emerged as a promising solution for motion tracking that has various applications in entertainment [1], human-computer interaction [2], healthcare [3, 4], etc. Among different types of such wearable devices, those using flexible sensors stand out for their advantages in long-term use scenarios. Specifically, flexible sensors can be easily integrated into wearable devices ( e.g. , sewn into ordinary clothing) while ensuring wearing comfort. These flexible sensors bend in response to human body movements (represented by joint angles), causing changes in their readings, which can be used for motion tracking [5].

In recent years, enabling wearable devices to be worn in non-fixed positions has become a crucial objective in order to make these devices more suitable for daily use [6]. However, this flexibility poses new challenges for deep-learning powered motion capture (mocap) systems. That is, the inevitable on-body displacement across wearing sessions can cause significant data distribution shifts, which degrades the performance of supervised learning models (Figure 1a).

A straightforward solution to the above problem is to collect a large dataset that covers a wide spectrum of wearing positions for model training. However, this approach significantly amplifies data collection costs, including equipment expenses, manpower, and time investments. Furthermore, any alterations in device design (sensor position, quantity, etc.) necessitate a fresh round of data collection, resulting in significant resource consumption and impeding swift product iteration. Another solution is to fine-tune the model against 'unseen' displacements with a small amount of labeled data under new displacements, a.k.a. , supervised domain adaptation. However, this is infeasible for our task as

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obtaining accurate joint angles ( i.e. , labels) requires advanced optical motion capture systems that are difficult to set up in real-world scenarios ( e.g., outdoor running, boating), hindering everyday use.

In this work, we address the above challenges by proposing a novel self-adaptive motion tracking network that can adapt itself to unknown displacements in an unsupervised way, using only a small amount of new sensor readings. Specifically, most existing unsupervised domain adaptation methods assume the source and target domains share identical feature distributions, which is not applicable to our task as our training set contains data with multiple displacements, while the test data has only one unknown displacement. To this end, we propose a novel solution that allows the alignment of test data with a subset of training data that also has one displacement, which encompasses three novel components: i) a light-weight learnable Affine Transformation layer whose parameters effectively model the distributional shifts caused by displacements; ii) a Fourier-encoded LSTM network that facilitates the learning of more frequency components for better pattern learning and identification; iii) a novel sequence discrepancy loss equipped with auxiliary regressors for unsupervised self-adaptation.

The contributions of our method are three-fold:

We propose a novel self-adaptive motion tracking network that can adapt to unknown onbody displacements of flexible sensors in an unsupervised way, allowing for long-term and daily motion tracking.

We propose three novel components for our network: i) a light-weight learnable Affine Transformation layer, ii) a Fourier-encoded LSTM network, and iii) a novel sequence discrepancy loss, which together efficiently reduce the distributional shifts of sensor readings caused by on-body displacements.

Extensive experiments demonstrate the superior performance of our method against the state-of-the-art methods of domain adaptation applied in our scenario.

2 Related Work

2.1 Domain Adaptation

Domain adaptation aims to mitigate the gap between the source and target domains so that models trained in the source domain(s) can be applied to the target domain(s). Traditional methods perform adaptation effectively by either reweighting samples from the source domain [7, 8], or seeking an explicit feature transformation that transforms the source and target samples into the same feature spaces [9, 10, 11, 12]. Subsequent studies have shown that deep neural networks can learn more transferable features for domain adaptation [13]. For example, Deep Domain Confusion (DDC) [14] first proposed the use of Maximum Mean Discrepancy (MMD) loss to align the feature distribution of the target domain with that of the source domain for the domain adaption of deep neural networks; Deep Adaptation Networks (DAN) [15] extends the idea to the use of multiple-kernel MMD; Deep CORAL [16] proposed CORAL loss [17] for adaptation; Chen et al. [18] propose a Higher-order Moment Matching (HoMM) method for better distribution discrepancy minimizing. Other works within deep neural networks also illustrate immense success in learning transferable

features [19, 20, 21]. In addition, recent studies show that adversarial learning also contributes to learning more transferable and discriminative representations [22, 23, 24, 25, 26, 27, 28].

In summary, most existing domain adaptation methods assume that the source and target domains share the same feature distributions and thus aim to minimize relevant distributional differences [29]. While in our task, the training set contains data with multiple displacements while the test data has only one unknown displacement, thus making the above-mentioned assumption invalid.

Addressing this issue, we propose a novel self-adaptive motion tracking network that tunes the parameters of an Affine Transformation layer with a novel sequence discrepancy loss, which can align the test data with a subset of training data that also has one displacement, achieving robust and accurate domain adaptation for flexible sensors.

2.2 Human Motion Capture

Human motion capture records human body movements and has been widely applied in entertainment, healthcare, sports, etc. Existing mocap solutions can be roughly classified into two categories: visionbased and sensor-based.

Vision-based mocap solutions make use of the latest deep learning techniques and have achieved great success in specific scenarios [30, 31]. However, they rely on good visual conditions and are inherently weak against textureless clothes and environmental problems ( e.g. , challenging lighting, occlusion). Most existing sensor-based mocap solutions rely on Inertial Measurement Units (IMUs) to record motion inertia / acceleration for the analysis of human posture [32, 33, 34]. Although robust against environmental conditions, the dense and tight placement of IMUs is intrusive and inconvenient, hampering performers from moving freely in their daily lives.

To this end, people turned to flexible sensor-based wearable devices indistinguishable from daily clothing. For example, Glauser et al. [35] designed a stretch-sensing soft glove and used it to interactively estimate hand poses with the aid of a deep neural network; Ma et al. [36] proposed flexible all-textile dual tactile-tension sensors for precise monitoring of athletic and form, illustrating their potential application in robust physical training analysis. Zhou et al. [37] uses a deep regressor to continuously predict the 3D upper body joints coordinates from 16-channel textile capacitive sensors.

2.3 Flexible Sensors

Given their advantages of bio-compatibility, high stretch-ability, lightweight, and ease of integration within clothing, flexible sensors have been used for long-term monitoring of human physical status, specifically motion capture [38], human-computer interfaces [39], soft robotics [40], etc. For human motion tracking, existing methods have explored the use of flexible sensors in tracking the motion of the upper body [41], fingers [35], lower limbs [42], elbow joints [43], and knee joints [44]. Along with such exploration, it has been noted that on-body displacement of sensors is inevitable as the device cannot be firmly worn at a fixed position across different sessions [45]. Additionally, due to the deformation characteristics of flexible sensors, it is fairly complicated to achieve robust motion tracking in the presence of placement deviation [46, 47].

In this work, we start with an exemplary scenario: an elbow pad with two flexible and stretchable sensors for elbow joint tracking, and address this gap by proposing a novel self-adaptive motion tracking network that can adapt to unknown on-body displacements in an unsupervised manner to achieve robust and accurate motion tracking.

3 Hardware

We design and develop a prototype by augmenting a standard elbow pad with two soft stretchable ( i.e. , flexible) sensors, which are placed on the olecranon side of the elbow. The pad offers a versatile size of 20 cm in length and 25 cm in circumference, accommodating a broad spectrum of users. The two sensors are placed 2 cm apart.

Our method aims to estimate the bending angle, θ , of an elbow joint (Figure 1) from the sensor readings of the two flexible sensors. The bending angle is defined as the angle in the sagittal plane

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between the humerus and the central line between the radius and the ulna. Fabric sensors are purchased as off-shelf products from ElasTech 1 . They are capacitive, i.e. , their capacitance increase with the stretch caused by the bending of the arm. The sensor readings are digitized to values in the range [0, 1023] and transmitted wirelessly via Bluetooth Low Energy at a frame rate of 50Hz.

4 Method

In our work, a motion tracking system using flexible sensors aims to construct a function f between sensor readings R and joint angles θ (Figure 1a) that: θ = f ( R ) . Naively, f can be approximated through supervised learning by fitting a model to collected ( R,θ ) data. While in practice, it is infeasible to collect enough ( R,θ ) data for training as R depends on the countless instances of on-body displacement of flexible sensors ( c, l ) , where c (deg) and l (cm) are circular and lateral displacement, respectively (Figure 1a). Addressing this issue, we propose a novel self-adaptive motion tracking network for flexible sensors that can efficiently adapt to unseen on-body displacements in an unsupervised manner. As Figure 2 shows, our network decomposes f into two parts:

where R 0 denotes the sensor readings with a limited number of displacements in the training dataset, R denotes the sensor readings with unknown displacements at the time of testing. Between them, f R→R 0 is implemented with a light-weight learnable Affine Transformation layer whose parameters can be tuned to align R to R 0 and thus adapt the network to 'unseen' displacements (Sec. 4.1); f R 0 → θ is implemented with a Fourier-encoded LSTM network to reduce the learning bias towards low-frequency functions and produce more accurate results (Sec. 4.2). The tuning of f R→R 0 is achieved by minimizing a novel sequence discrepancy loss equipped with auxiliary regressors (Sec. 4.3).

4.1 Learnable Affine Transformation

The proposed learnable Affine Transformation layer is an efficient implementation of f R→R 0 . Specifically, it models the linear transformation components of flexible sensors in a motion tracking system:

Initial Stretch , which is mainly affected by lateral displacements (Figure 1(b)) and can be modeled with the additive bias vector in affine transformation.

Stretching Range , which is mainly affected by circular displacement (Figure 1(c)) and can be modeled with a scaling matrix in affine transformation.

Accordingly, let X = [ r 1 , r 2 ] T be the sensor readings, W s = diag [ s 1 , s 2 ] be the scaling matrix, B = [ b 1 , b 2 ] T be the bias vector, we have:

Both W s and B are learnable in the adaptation stage (Sec. 4.3) for the adaptation to unknown displacements.

4.2 Fourier-encoded LSTM Network

We implement f R 0 → θ by augmenting a standard LSTM sequence prediction network with a Fourier Feature Encoding (FFE) layer, which models the nonlinear mapping from flexible sensor signals to joint angles. Our key insight is that FFE helps to reduce the learning bias of deep neural networks towards low-frequency functions [48] that leads to underfitting of f R 0 → θ .

The FFE layer used in our work is as follows:

where x ∈ X is the input, A > | max( X ) -min( X ) | so that x ′ and x are in one-to-one correspondence. Please note that our Fourier-encoded LSTM network is only trained in the pretraining stage and fixed during adaptation (Figure 2).

4.3 Unsupervised Adaptation to Displacements

Algorithm 1 Unsupervised Adaptation to Displacements

Input : Pretrained model f with auxillary regressors; Scaling matrix W s and Bias vector B of the affine transformation layer in f ; Test-time sensor readings R ; Batch size n ;

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: Training epoch e ; Learning rate η ;

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5: W s ← W s -η ∂ L ( ˆ Θ) ∂W s ; B ← B -η ∂ L ( ˆ Θ) ∂B

6: end for

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8: return W s , B

As mentioned above, our network decomposes the mapping f into two parts f R→ θ = f R→R 0 · f R 0 → θ . Between them, the challenging nonlinear components are well-approximated by f R 0 → θ , which greatly simplifies our adaptation to tuning the parameters of the Affine Transformation layer of f R→R 0 . Intuitively, we aim to align the distribution of R to that of R 0 so that f R 0 → θ can accurately map R to joint angle θ . Naively, this can be achieved by directly minimizing a distributional difference

loss between R 0 and R or maximizing the prediction accuracy when using R . However, neither approach is effective for our task as i) the former assumes R and R 0 share the same distribution after the affine transformation, which is incorrect because R 0 (the training set) contains data with multiple displacements while R is collected with a single unknown displacement; and ii) the latter requires ground truth joint angles θ of R , which are unavailable during use. Addressing this challenge, we propose a novel sequence discrepancy loss equipped with auxiliary regressors as follows.

Sequence Discrepancy Loss. We observed that the domain gaps among different displacements are particularly evident when the elbow is bent and less when the elbow is flexed. Consequently, the domain gaps are not evenly distributed over time during elbow movement, leading to the discrepancy among different regressors that estimate the same joint angle θ t with different choices of time windows, where t denotes the time step in a motion sequence. Our key idea is that the adaptation to unknown displacements can be achieved by minimizing such discrepancy. Specifically, let n be the length of the time window for the main regressor, we have

where ˆ θ (0) t is its joint angle estimate. Similarly, we include additional n -1 auxillary regressors that predict θ t with different time windows as:

where ˆ θ (0) t ≈ ˆ θ (1) t ≈ ... ≈ ˆ θ ( n -1) t for R 0 used in supervised pretraining, i =1,...,n-1. However, when R is used, discrepancies occur among regressors and we tune the affine transformation parameters (Sec. 4.1) to minimize such discrepancies. Specifically, let Θ ( k ) = mean( ˆ θ ( k ) 1 , ˆ θ ( k ) 2 , . . . , ˆ θ ( k ) t ) be the mean of joint angle estimates of regressor k , ˆ ϕ ( k ) t = ˆ θ ( k ) t -min t ( ˆ θ ( k ) ) max t ( ˆ θ ( k ) ) -min t ( ˆ θ ( k ) )+ ε be the normalized joint angles, ε = 1 for numerical purpose, we define:

Our overall sequence discrepancy loss is a weighted sum of L mean and L shape :

where α ∈ [0 , 1] is a weighting hyperparameter. Alg. 1 shows the pseudo-code of our adaptation algorithm.

5 Experimental Results

5.1 Experimental Setup

Dataset and Metrics The dataset used in this paper consists of sensor readings and joint angles collected by a single user wearing the augmented elbow pad (Sec. 3) while performing elbow flexion. The joint angles are computed using the 3D positions captured by a Nokov motion-capture system at a rate of 60 FPS. As instances of different on-body displacements, we collect 11 groups of data with circular displacements c ∈ {-5 , 0 , 5 } and lateral displacements l ∈ {-2 , -1 , 0 , 1 , 2 } . For each group, we collected data for 8 consecutive elbow flexion. In total, we collected 5,310 frames of data across the 11 valid groups. Among the 11 groups, we randomly selected 5 of them as the training set D train and used the rest 6 groups as the test set D test . We have obtained ethical approval for the publication of both datasets and results.

We use the Mean Absolute Error (MAE) of predicted joint angles (degrees) as our main evaluation metric. For each experiment, we repeat 20 times and report their mean and standard deviation (std). Unless specified, we use the same setups for all experiments.

Training Details Our model is trained in two stages: supervised pretraining and unsupervised adaptation. For the supervised pretraining, we employ an MSE loss:

where ˆ θ ( k ) t is the output of regressor k at time step t , θ t is the corresponding ground truth joint angle. We use an Adam optimizer with a learning rate of 1 e -3 , β 1 = 0 . 9 , β 2 = 0 . 999 , and training epoch e = 30 . For the adaptation, we use an Adam optimizer with a learning rate of 5 e -3 and a weight decay of 0 . 001 , β 1 = 0 . 9 , β 2 = 0 . 999 , and training epoch e = 20 . We use n = 10 for both pretraining and adaptation. All experiments were conducted on a desktop PC with an AMD Ryzen 3950X CPU and an NVIDIA RTX 3080 GPU. Please see the supplementary materials for the details of network architectures.

5.2 Comparisons with SOTA

As Table 1 shows, we compare our method with state-of-the-art (SOTA) domain adaptation methods. For a fair comparison, we have adapted the official code provided by the authors to share the same input and output format as ours. Please see the supplementary materials for more details on the implementation of SOTA methods. It can be observed that our method outperforms all SOTA methods, which demonstrates its effectiveness.

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To gain more insights, we visualize the results of our method and its two strongest competitors, AdvSKM [27] and HoMM [18], as time series in Figure 3. The results show that our method fits the ground truth with a closer distance than other SOTA methods.

Comparison with Supervised Learning

To demonstrate the effectiveness of unsupervised learning in our task, we tune the parameters of Affine Transformation layer by supervised learning ( i.e. , supervised domain adaptation) to obtain its 'upper bound'. Even trained in an unsupervised manner, our method achieves results comparable to its supervised version (Table 1). This difference of 1.5 degrees indicates that our unsupervised learning approach is effective.

Experiments on different participants To further demonstrate the generalization ability of our method, we conducted experiments with five additional participants of varying body types (see the supplementary materials for body profile details). Each of these five participants wore the devices in three supervised on-body positions and performed three distinct physical activities sequentially: ping-pong , basketball , and boxing . Each activity lasted for a minimum duration of one minute. In total, this comprehensive evaluation approach yielded 15 (5 participants × 3 on-body displacements) unique data segments across all participants, comprising 81,848 total frames. For each participant,

we conducted 5 times 3-fold cross-validation experiments and recorded the average result. As Table 2 shows, our method achieves optimal or near-optimal results for all five additional users, indicating the effectiveness of our method against user diversity.

5.3 Ablation Study

To demonstrate the effectiveness of the three novel techniques proposed, we conduct an ablation study as follows:

FFE : Remove the Fourier Feature Encoding (FFE) layer from the LSTM network.

Affine : Remove the Affine Transformation layer and tune all the parameters of the network during adaptation after supervised pretraining. For the adaptation, we use a weighted sum of two loss terms: i) a supervised loss L mse applied on the training data; and ii) our sequence discrepancy loss L applied on the test data.

SD Loss : Replace our Sequence Discrepancy (SD) loss with a more naive and stricter version:

5.4 Justification of Motivation

Affine Transformation Figure 4 demonstrates the effective alignment of the target and source domains with our method. As shown in Figure 4a and 4b , although light-weight, the proposed Affine Transformation layer effectively aligns the sensor reading distributions of test data (blue) to those in the training set (orange), thereby significantly improving the accuracy of joint angle estimation.

Sequence Discrepancy Loss The design rationale of this component is rooted in the insight that our sequence discrepancy loss L shares a similar optimization landscape with the estimation error of joint angles during optimization (Figure 5). This consistency effectively offers the advantage of

We have enumerated all combinations of the three components proposed. As Table 3 shows, it can be observed that: i) Ours achieves the best performance, indicating the effectiveness of the three novel techniques proposed; ii) FFE and affine transformation improve the performance in all cases; iii) the effectiveness of SD loss relies on affine transformation. In both cases with and without FFE, the co-existence of affine transformation and SD loss show better performance than using SD loss alone (Ours over Case 6, Case 5 over Case 3).

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leading the network optimization toward the optimal point. As shown in Figure 6, i) before adaptation, the outputs of different regressors significantly differ from each other, leading to a high sequence discrepancy; ii) after adaptation, the discrepancy among regressors reduced and the accuracy of joint angle estimates for all regressors was improved significantly at the same time.

Fourier Feature Encoding Existing literature shows the existence of spectral bias: the tendency of neural networks to prioritize learning low-frequency functions [48]. Figure 7a shows the frequency distribution difference between the prediction and ground truth. Without the component of FFE, the network output shows a large deviation in the region of low-frequency domain from the ground truth (on the left side of this plot). This over-emphasis on the low-frequency components inevitably dominates the prediction capability and weakens the reconstruction of the high-frequency signals.

Our experiment confirms that Fourier Feature Encoding (FFE) reduces the spectral bias of neural networks. Therefore, as Figure 7 shows, our method can approximate the mapping between sensor readings and joint angles in a more faithful way.

















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6 Limitations and Future Work

Limitations The performance of the proposed method is dependent on the quality of the data in both the training and test sets. When the data quality is poor ( e.g. , noisy), the error in joint angle prediction may still be high even if the sensor readings are well aligned (see Figure 8a and 8b).

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Specifically, according to the frequency characteristics of elbow movement, we define the sensor signal greater than 5Hz as noise, and calculated the signal-to-noise ratio (SNR) for the sensor signals within the 11 data sets collected from a single user. Furthermore, we compiled the mean absolute error (MAE) for each set after implementing our adaptive approach. It can be found that our proposed method exhibits significant effectiveness when the SNR exceeds 10 dB. Conversely, maintaining satisfactory outcomes becomes challenging when the SNR falls below 10 dB (see the supplementary materials for the visualization).

Future Work As abovementioned, we believe that extending our method to learning with noisy data would be interesting future work. In addition, our work opens up a number of research directions for future efforts. First, in most domain adaptation methods, measuring the similarity between the distribution of the target domain and the source domain is a key step. While in our work, we found that the output of the auxiliary regressors can be used as an indicator of data distribution shifts from the source domain, and verified the feasibility of domain adaptation using only target domain data in flexible sensor applications. In future work, we hope to implement this idea in more diverse applications. Second, although we have only used translation and scaling, this work proves affine-based domain adaptation to be a promising solution to mitigate the data distribution shifts of flexible sensors. The effectiveness of rotation and reflection remains to be studied in our future work.

7 Conclusion

Our work proposes a novel self-adaptive motion tracking network to address the challenging data distributional shifts caused by on-body displacements of flexible sensors. To mitigate the effects of such displacements, we propose three novel techniques. First, we propose an Affine Transformation layer that can remap shifted data distributions to those in the training set efficiently. Second, we propose a Fourier-encoded LSTM network that can learn richer frequency components in the input signals and thus improves the accuracy of joint angle estimation. Finally, we propose a Sequence Discrepancy loss equipped with auxiliary regressors that can adapt the parameters of Affine Transformation effectively in an unsupervised manner. Experimental results show that our method can effectively adapt to unknown displacements of flexible sensors worn at different positions.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn the context of ubiquitous sensing, the paper addresses the issue that on-body devices cannot be firmly worn at a fixed position across different sessions by adapting to unknown displacements. The authors propose three main contributions: (1) A transformation layer adapts to unknown displacements, (2) an LSTM networks identifies patterns, and (3) a discrepancy loss for unsupervised learning.\n\nSTRENGTHS:\n- The problem setup is well motivated. The addressed problem exists in many application areas and research fields.\n- The paper is well written and easily understandable.\n- The soundness of the technical claims are good and the experiments support the proposed method.\n- The authors will make the datasets and source code publicly available.\n\nWEAKNESSES:\n- Section 2.3 contains introductory applications that is repeated from Chapter 1, and hence, can be integrated in the Introduction.\n- Experiments are only performed on this specific dataset. It is not clear, whether the proposed method can be generalized to other applications, e.g., on datasets such as HHAR, UCI HAR, WISDM, and uWave (time series domain adaptation datasets).\n- Experiments in Sec. 5.1 should also cover data from more than one participant\n- References are missing and state-of-the-art methods on which experiments are performed are limited. The following methods can also be considered for comparison: MMCD [1], MMDA [2], DDC [3], DAN [4], HoMM [5], CDAN [6], DIRT-T [7], CoDATS [8], AdvSKM [9], and Sinkhorn methods [10,11]. \n\nIn my opinion, the paper needs an extensive re-write if published at an ML-venue such as NeurIPS. From a methodological perspective the main contribution is the novel sequence discrepancy loss (as (1) learnable affine transformations and (2) Fourier-encoded LSTMs or similar ideas already exist [12]). The paper should be re-written around this main contribution and the on-body placement use-case should be one dataset of many time series data set where the method should be applied on.\n\n**Some minor points:**\n- L34ff: But if the features are transformed accurately they still share the same feature space, right?\n- L101: it is uncommon to name a specific journal of a citation within the main text\n- L115: increase[s]\n\n**Missing references:**\n- [1] Naimeh Alipour and Jafar Tahmoresnezhad: Heterogeneous Domain Adaptation with Statistical Distribution Alignment and Progressive Pseudo Label Selection. In Applied Intelligence, volume 52, pp. 8038–8055, October 2021. doi: 10.1007/s10489-021-02756-x.\n- [2] Mohammad Mahfujur Rahman et al.: On Minimum Discrepancy Estimation for Deep Domain Adaptation. In Domain Adaptation for Visual Understanding, Springer, Cham., January 2020. doi:10.1007/978-3-030-30671-7_6.\n- [3] Eric Tzeng et al.: Deep Domain Confusion: Maximizing for Domain Invariance. In arXiv preprint arXiv:1412.3474v1 [cs.CV], December 2014.\n- [4] Mingsheng Long et al.: Learning Transferable Features with Deep Adaptation Networks. In Proc. of the Intl. Conf. on Machine Learning (ICML), volume 37, pp. 97–105, July 2015. doi: 10.5555/3045118.3045130.\n- [5] Chao Chen et al.: HoMM: Higher-Order Moment Matching for Unsupervised Domain Adaptation. In Proc. of the AAAI Conf. on Artificial Intelligence (AAAI), volume 34(4), pp. 3422–3429, April 2020. doi: 10.1609/aaai.v34i04.5745.\n- [6] Mingsheng Long et al.: Conditional Adversarial Domain Adaptation. In Advances of Neural Information Processing Systems (NIPS), volume 31, pp. 1647–1657, December 2018. doi: 10.5555/3326943.3327094.\n- [7] Rui Shu et al.: A DIRT-T Approach to Unsupervised Domain Adaptation. In Intl. Conf. on Learning Representations (ICLR), 2018.\n- [8] Garrett Wilson et al.: A Survey of Unsupervised Deep Domain Adaptation. In Proc. of the ACM Trans. on Intelligent Systems and Technology (TIST), volume 11(5), pp. 1–46, October 2020. doi: 10.1145/3400066.\n- [9] Qiao Liu and Hui Xe. Adversarial Spectral Kernel Matching for Unsupervised Time Series Domain Adaptation. In Proc. of the Intl. Joint Conf. on Artificial Intelligence (IJCAI), pp. 2744–2750, August 2021. doi: 10.24963/ijcai.2021/378.\n- [10] Felix Ott et al.: Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift. In Proceedings of the ACM International Conference on Multimedia (ACMMM), pages 5934–5943, Lisboa, Portugal, October 2022. doi:10.1145/3503161.3548167.\n- [11] Huan He et al.: Domain Adaptation for Time Series Under Feature and Label Shift. arXiv preprint arXiv:2302.03133, February 2023.\n- [12] Zhang et al.: Learning Long Term Dependencies via Fourier Recurrent Units. ICML 2018.\n\nQUESTIONS:\nnone.\n\nLIMITATIONS:\nThe limitation of this work is that it is not clear whether the proposed approach can be applied to other time series domain adaptation applications or the method is specifically designed for this on-body displacement of flexible sensors and overfitted on these dataset.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2023-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper shows an approach for adaptive learning to track motion trajectories from elbow pad sensor, especially concentrating on modelling of sensor displacements in unsupervised manner during the operation. Tracking method is based on multi-layer neural network architecture with learnable affine layer, Fourier feature encoded LSTM, and multiple outputs regression layers. Furthermore, novel sequence discrepancy loss is introduced to reduce data shift caused by sensor displacements. Proposed techniques are evaluated on collected real datasets with ablation study and comparison to other domain adaptation methods from the literature.\n\nSTRENGTHS:\nThis is application oriented paper with novel combination of existing techniques (i.e., affine transformation layer for data shift, Fourier LSTM for pattern matching, and sequence discrepancy loss with aux. regressor for unsupervised/self-adaptive learning) for specific problem, which is evaluated with SOTA comparison and ablation study to work in practice (see also weaknesses). Paper is well-structured and its main processing pipeline is illustrated with related formulation.\n\nSummary of strengths\n- Practical solution for (very specific) problem in domain adaptation\n- Good combination of different techniques for new application \n- Empirical evaluation with comparison and ablation study\n- Clearly illustrated processing pipeline and formulation\n\nWEAKNESSES:\nPaper only considers very specific problem with one type of sensor and small dataset. It is difficult to assess if this could generalise to different settings. Although there are comparison to SOTA models, paper lacks some basic baseline from signal processing field, e.g., low dimensional data could be estimated using state-space models such as extended kalman or particle filters or at least give more justification of need for learnable methods with adaptive capabilities. Also, most of the SOTA methods presented in the paper are designed for the image data domain adaptation, not for low-dimensional time-series data, so there could be some more related approaches. In addition to utilisation of more comprehensive dataset, paper lack detailed analysis of boundaries in relation to how much noise and sensor displacement it could handle.\n\nSummary of weaknesses \n- Very narrow application of domain adaptation (could it be extend to other settings or problems)\n- Small datasets (one person, four in supplement)\n- Lack of basic baseline (e.g., state space models (EKF, PF etc.))\n- Missing the study of data noise and/or boundaries for amount of sensor displacement handled \n- Most of the compared SOTA methods designed for different data (e.g., images)\n\nQUESTIONS:\n- How well the prior SOTA methods evaluated in the paper compares to particular time-series type of domain shift?\n- What are the boundaries of sensor displacement (i.e., how much noise or data shifts method can handle)?\n\nLIMITATIONS:\nSome of the limitations of proposed approach are shortly analysed in relation to specific issues such as data quality.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2023-07-04"}

comments: SUMMARY:
Flexible sensors are useful for tracking human status as wearable systems, but they can become displaced when worn, causing challenges for machine learning algorithms. The proposed solution of this paper is a self-adaptive motion tracking network that includes a learnable Affine Transformation layer, a Fourier-encoded LSTM network for pattern identification, and a sequence discrepancy loss with auxiliary regressors for unsupervised tuning of Affine Transformation parameters.

STRENGTHS:
1. The proposed method of this paper is useful for real-world applications.
2. The proposed method is intuitive and easy to follow.
3. The experiment results show the effectiveness of their method.

WEAKNESSES:
1. This paper mainly works for the real-world sensor. I would like to know how it works in real-world applications. 
2. In the experiments, the author should compare more advanced methods, for example, the least method the author compared is published in 2020. I would like to know how this framework works together with state-of-the-art modules, for example, transformer.

QUESTIONS:
Please answer 2 of the weakness

LIMITATIONS:
Please answer 2 of the weakness
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2023-07-05

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents an approach to self-adaptive motion tracking using on-body, flexible sensors that in real world applications may be subject to displacements. The authors present a network that contains a component that automatically learns an affine transformation between training data (with annotations in form of sensor data with their corresponding target joint angles), a Fourier encoded LSTM part for the actual pattern recognition, and a bespoke loss function that uses auxiliary regressors for automated, unsupervised tuning of the parameters of the aforementioned affine transformation. The authors evaluate their method in practical experiments where participants wore elbow sleeves that contain flexible sensors and performed various bend movements. The objective of the evaluation is to measure the accuracy of the predicted joint angles for motion tracking applications. Comparisons to related SOTA models are made and it is shown that the proposed method outperforms all other analyzed models in terms of much better accuracy (distance from ground truth). An ablation study sheds light on the contributions of the individual components of the network and the supplementary material provides details on the implementations, data characteristics etc.\n\nSTRENGTHS:\nThis paper is very well written, technically sound, and the experimental results are convincing. I applaud the author for what seems like rigorous work and great thoroughness in execution. Well done. The papers is very application driven, which is fine of course. The tracked problem is relevant for the targeted application domain of motion tracking using body-worn sensors. As such, I can see that the paper may have impact in this field. The authors carefully designed a technical solution in form of a network that specifically, and successfully, addresses domain specific aspects. The experimental evaluation is relevant at least in parts and comparisons to related work / SOTA are done in a very careful and thorough manner. The reported results look promising.\n\nWEAKNESSES:\nThe experimental evaluation looks a bit limited. While I appreciate the range of displacement that were tackled, the number of participants is a bit small, and so are their individual recordings. I would imagine that the displacements are dependent on a number of factors including the physique of the participants and the tasks performed. Not much is said about either of these and I fear that the promising results may not (or maybe they will — hard to tell!) generalize too much.\n\nOn a higher level note: “application papers” typically have a more challenging time at NeurIPS. I do not want to discount the paper merely by the fact that it is mainly an application paper. However, the presented method is very much tailored to motion tracking and I cannot see how the method itself would generalize beyond this. This is not a criticism per se but rather an assessment of the potential impact that paper might have to the broader NeurIPS audience, which would judge as rather limited. I wonder if the authors could branch out their method beyond the specific motion tracking application here? They argue through domain shifts and such, which is a hot topic in the field, but I fail to see how this domain adaptation etc. community would gain much insight from this paper.\n\nQUESTIONS:\nSee my comment above re limited impact beyond the particular application domain. I wonder if the authors could elaborate on this a bit. \n\nAlso, see my concern about the evaluation. Can the authors provide more information about their participants?\n\nLIMITATIONS:\nDiscussion on limitations is there and ok.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 4, "DATE": "2023-07-26"}