Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in sEMG Analysis
Authors: Weiyu Guo
Ziyue Qiao
Ying Sun
Yijie Xu
Hui Xiong
Venue: ICML 2025 poster (2025)
Keywords: Gesture Recognition
Human Brain Interaction
sEMG
Short Term Enhanced Transformer
Primary area: applications->neuroscience_cognitive_science

Abstract: Gesture recognition based on surface electromyography (sEMG) has been gaining importance in many 3D Interactive Scenes. However, sEMG is easily influenced by various forms of noise in real-world environments, leading to challenges in providing long-term stable interactions through sEMG. Existing methods often struggle to enhance model noise resilience through various predefined data augmentation techniques.
In this work, we revisit the problem from a short-term enhancement perspective to improve precision and robustness against various common noisy scenarios with learnable denoise using sEMG intrinsic pattern information and sliding-window attention. We propose a Short Term Enhancement Module(STEM), which can be easily integrated with various models. STEM offers several benefits: 1) Noise-resistant, enhanced robustness against noise without manual data augmentation; 2) Adaptability, adaptable to various models; and 3) Inference efficiency, achieving short-term enhancement through minimal weight-sharing in an efficient attention mechanism.
In particular, we incorporate STEM into a transformer, creating the Short-Term Enhanced Transformer (STET).
Compared with best-competing approaches, the impact of noise on STET is reduced by more than 20\%. We report promising results on classification and regression tasks and demonstrate that STEM generalizes across different gesture recognition tasks. The code is available at https://anonymous.4open.science/r/short_term_semg.

Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in sEMG Analysis
Weiyu Guo Ziyue Qiao
Ying Sun
13
Yijie Xu
Hui Xiong 13
Abstract
1. Introduction
Gesture recognition based on surface electromyo- graphy (sEMG) has been gaining importance in many 3D Interactive Scenes. However, sEMG is easily influenced by various forms of noise in real-world environments, leading to challenges in providing long-term stable interactions through sEMG. Existing methods often struggle to en- hance model noise resilience through various pre- defined data augmentation techniques. In this work, we revisit the problem from short-term enhancement perspective to improve precision and robustness against various common noisy scenarios with learnable denoise using sEMG in- trinsic pattern information and sliding-window attention. We propose the Short Term Enhance- ment MSdule(STEM), which can be easily inte- grated with various models. STEM offers several benefits: 1) Noise-resistant, enhanced robustness against noise without manual data augmentation; 2) Adaptability, adaptable to various models; and 3) Inference efficiency, achieving short-term en- hancement through minimal weight-sharing an efficient attention mechanism. In particular, we incorporate STEM into transformer, creating the Short-Term Enhanced Transformer (STET). Com- pared with best-competing approaches, the im- pact of noise on STET is reduced by more than 20%. We report promising results on classifica- tion and regression tasks and demonstrate that STEM generalizes across different gesture recog- nition tasks. The code is available at https: //gi thub. wtw.cooo//oo/o http//gwweiiy/// wwwpweeiyysshottteeee teee---esssseeeeee semg.
Surface Electromyographic (sEMG) is a non non-invasive tech- nique for monitoring muscle neuron firing, which is effec- tive way to capture human motion intention and has shown great application potential in the field of numan-computer interaction (HCI) (Xiong et al., 2021; Liu et al., 2021c; Guo et al., 2024). A schematic diagram of the EMG-based HCI System shown Figure 1. Compared to traditional HCI channels, sEMG has the advantages of being generated prior to actual motion (50-150 ms), containing rich motion inten- tion information, and being easy collect (Sun et al., 2020). Therefore, increasing interest has been in exploring EMG- based motion track (Liu et al., 2021b; Guo et al., 2023) and pathological analysis.
EMG DATA COLLECTION
MOTION INTENTION
HUMAN-MACHIVG INTERACTION
PREDICTION
electrodes
EMG
Figure 1: Schematic Diagram of EMG- based Human Human-Computer Computer Interaction System.
Recently, deep learning models for time series have devel- oped rapidly and are widely used (Dai et al.; Zhang et al., 2023a; Xu et al.; Zhang et al., 2021). 1). By treating sEMG as time series, deep sequential models (Bi et al., 2019; Tsinganos et al., 2019; Becker al., 2018; Li al., 2021; Du et al., 2017: Zhang al., 2021) have been applied to sEMG modeling. For example, Zhang et al. (2022) employ multi-task encoder-decoder framework to improve the ro- bustness of 4EMG-based Sign Language Translation (SLT), while Rahimian et al. (2021) employ a Vision Transformer (ViT)-based architecture (TEMGNet) to enhance the accu- racy of sEMG-based myocontrol for prosthetics. Although these methods show improved performance over traditional approaches, they process sEMG signals generic time- series data, without tailoring their designs to the unique characteristics of tivity to external noise and interference. This oversight leads to challenges in handling low signal-to- ratios caused by skin condition changes and interference, and limits the
'Thrss of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China "School of Computing and Information Technology, Great Bay University, Dongguan, China Department of Computer Science and Engineer- ing, The Hong Kong University of Science and Technology. Hong Kong SAR, China. Correspondence to: Ying Sun <yings ust hk>, Hui Xiong <xionghui @ hk..
as high variability and sensi-
Proceedings of the ndnd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in sEMG Analysis
ability capture subtle but critical motion information. As result, the robustness and accuracy of existing models remain significantly constrained.
We propose STEM, a learnable, adaptable, and noise- resistant module to enhance short-term features. Integrat- ing STEM into various neural network architectures yields consistent and significant performance improvements; We introduce sEMG Signal Masking into the self- supervised Intrinsic Pattern Capture module, enabling the model to exploit sEMG variability;
Processing sEMG signals is challenging due to the complex
noise mixed in the skin's surface and the presence of pat-
terns across various time scales. Existing works, mainly
focusing on long-term sequences, have used transformers to
We conduct comprehensive experiments on the largest available wrist sEMG dataset, demonstrating that our method outperforms existing approaches in both accuracy and robustness. Furthermore, short-term enhancement can generalize to other architectures such as Informer.
treat SEMG as a typical time series, aiming to enhance long- term dependencies. These approaches overlook the critical features that are present on short-term scales. Short-time scale features are important in sEMG analysis, as they aid in distinguishing subtle movements and facilitate the removal of variable noise. For example, gestures like Index Finger Extension (IFE) and Middle Extension (ME), while simi- lar in global sEMG patterns, can be differentiated through localized short-term signal variations.
2. Related Work
The EMG-based Intention Prediction of Human Motion can be broadly divided into model-based and data-driven methods. Model-based methods typically combine disci- plines such as kinesiology, biomechanies, and human dy- namics to explicitly model the relationship between EMG and outputs (such as joint angles and forces). The model often includes specific parameters, such as joint positions and bone-on-bone friction, that need to be repeatedly exper- imented with and adjusted until the desired performance is achieved. In terms of parameter selection and determination, model- methods can be further divided into kinematic models (Borbély & Szolgay, 2017), dynamic models (Koike & Kawato, 1995; Koirala et al., 2015; Liu et al., 2015), and muscle-bone bone models (Wang& & Buchanan, 2002; Zhao et al., 2020; Yao et al., 2018). Clancy et al. (2012) used a non- linear dynamics model to identify the relationship between constant posture electromyography and torque at the elbow joint. Hashemi et al. (2012) used the Parallel Cascade Iden- tification method to establish mapping between forearm muscles and wrist forces. However, model- methods have a large number of parameters that are difficult to mea- sure directly. Currently, only simple motion estimation with a limited number of joints and degrees of freedom is pos- sible. In contrast model-based approaches, data-driven methods do not require the measurement of various parame- ters. Recently some researchers have begun to use temporal deep learning models to extract motion information from sEMG (Lin et al., 2022; Zhang et al., 2022; Guo et al., 2021; Lin et al., 2024; Chen et al., 2024; Jiang et al., 2025). Lin et al. (2022) proposed a BERT-based structure to predict hand movement from the Root Mean Square (RMS) fea- ture of the sEMG signal. Rahimian et al. (2021) proposed a novel Vision Transformer (biT)- neural network archi- tecture to classify and recognize upper-lima hand gestures from sEMG for use in myocontrol of prostheses. However, these methods have neglected the modeling of short-term dependencies and have not considered the inherent variabil- ity in sEMG signals. Furthermore, there is lack of research in this field aimed at improving noise robustness.
To this end, in this paper, we present lightweight but pow- erful module called Short-Term Enhanced Module (STEM) which utilizes sliding window attention with weight sharing to capture short-term features. Building on STEM, we fur- ther propose the Short-Term Enhanced Transformer (STET). STET leverages STEM to capture local signal changes, enhanc- ing noise resistance, and then combines STEM with -ong-term features, further improving accuracy for downstream pre- dictions. Furthermore, to enhance model robustness with minimal annotation, we propose a self-supervised paradigm based on sEMG Signal Masking to leverage the inherent variability sEMG signals.
Finally, we conducted extensive experiments the largest public sEMG datasets. The experimental results show that STET surpasses existing methods by significant margin in both gesture classification and joint angle regression tasks for single-finger, multi-finger, wrist, and rest gestures. Meanwhile, STET achieves strong robustness even when trained on pure data and tested on noisy data. Compared with best-competing approaches. the impact of noise on STET is reduced by more than 20%. Moreover, through visualizations, we show that the long-term and short-term fcatures are complementary in sEMG- based gesture recog- nition tasks, and the fusion of the two features can make the classification boundary more obvious. This clearly demon- strates that short-term information is critical for sEMG- based gesture recognition and will provide new design paradigm for future sEMG model design. In particular, we have deployed STET as an important functional component in our HCI system, which can offer a more intuitive and effective experience. Our real-world deployment is shown in Figure in the appendix.
To the best of our knowledge, we are the first to explic- itly emphasize short-term features in bEMG-based gesture recognition. Our main contributions are as follows:
2

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Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in sEMG Analysis
Long Term Enhanced Module (Sec 4.3.1) Short-Term Enhunced Module (Sec 4.3.2)
(Sec
Gestures
Lom lom,
len, 1/2 feng
Gesture Reconstruction
sEMG Intrinsic Variation Capture Medule
Dense Layer
Lettyg Lemg
1h 1/4 lena
Concat
Segment by stiding windows
Linear Projection
Cono 10
Cono 1/
SEMG Sigraal Masking
[P
Ean
Pr. Pex
I
Emb.
Emb.
H
Pipeline (See 4.3)
Emd.
Long Term Enhanced Module
SEMG Intrinsic Variation Capture
Position Encoding
Fusion
Module
Emsh
Short Term
Enhanced Module
(0) Self-Supervisee Pre-training based Signal Masking
(b) Sbort- Term Enhamced Transformer
Figure 2: Overview of STET. The sEMG signal is encoded using the sEMG Intrinsic Pattern Capture module, which is first pre trained via sEMG signal Masking. A long-term and short-term enhanced module improves sEMG representations. An asymmetric optimization strategy addresses biases and imbalances in gesture recognition through an asymmetric classification loss.
3. Preliminaries
and x; = [ T2,2; .... represents the signal, vector of e sensors, where 2iy is the signal value of the j-th sensor in the i-th time step.
3.1. Dataset
We conduct the experiments on the GRABMyo (Pradhan et al., 2022) and the Ninapro DB2 (Atzori et al., 2014) datasets, which are the largest and most widely used sEMG datasets and have great potential for developing new genera- tion human-machine interaction based on sEMG signals.
4. Technical Details
4.1. Model Overview
Figure 2 illustrates the overview of our proposed framework for gesture recognition, which contains three components: (1) The sEMG Intrinsic Pattern Capture module encodes the sEMG signal sequence into the hidden sEMG repre- sentations. A pre-training model with a segment masking strategy and MSE reconstructing loss is proposed to learn inherent variability from the sEMG signals into the model's parameters. (2) The Long-term and Short-term Enhanced module uses two decoupling heads to extract the long-term and short-term context information separately, which im- proves the sEMG representations in preserving both the global sEMG structure and multiple local signal changes of the sEMG. (3) The Asymmetric Optimization strategy addresses the problems of sample biases and imbalance in gesture recognition via an asymmetric classification loss, which can make the model focus on hard and positive sam- ples to improve the recognition.
Data processing. The subjects performed 17 gestures of hand and wrist (including a rest period sEMG) according to the prompts on the computer screen. Each gesture was repeated 7 times, each lasting 5 seconds. To avoid muscle fatigue, rest 10s between repetitions. In the following ex- periments, we use five repetitions as the training set and two repetitions as the test set. Bandpass filtered between 10 Hz and 500 Hz with gain of 500 was adopted the raw signal. We use the difference between the corresponding electrodes in the two loops as the input signal for our model. To improve the convergence speed of the model, we use two methods (Max-Min normalization, μllaww normalization) to normalize the data (Rahimian et al., 2020; Recommendation, 1988). After normalization, we use time-sliding window to split samples. We set the window size as 200ms, and the overlap of adjacent windows is 10ms. ft-law normaliza- tion can logarithmically amplify the outputs of sensors with small magnitudes, which results in better performance than linear normalization.
4.2. sEMG Intrinsic Pattern Capture Module
4.2. 1. SEMG SIGNAL ENCODING
Given the sEMG signal sequence X = [x], X2, x.). we first project each signal x; E R into hidden embedding via a transformation matrix and add each signal embed- ding with an absolute position embedding. Then, we feed the output sequence into a I--layer Transformer and obtain
Definition 3.1 (sEMG Signal Sequence). An sEMG signal sequence is defined as temporal signal sequence sampled by multiple sensors from human wrist, which can be for- mulated as X = [x1, X2, xe), where is the time window
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Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in sEMG Analysis
). which incorporate temporal context signal information for each position in the sequence.
the output signal embeddings X = { (1)
x
positions for each sample: The optimization objective Lpp of pre-training can be written as follows:
min
Σ 1=0 1=
1 = (āā - =in)? (1)
4.2.2. SEMG SIGNAL. MASKING
M
After the sEMG signal-extractin module is constructed, we aim to use pre-training to exploit the intrinsic pattern and temporal semantics disclosed by the unlabeled sEMG sig- nals (labeling sEMG is time-consuming and labor-intensive) and give good initialization for the model parameters, then avoid the model focusing on some noisy features in the supervised learning task to over-fitting some local mini- mums. Thus, we propose sEMG Intrinsic Pattern Capture based on signal masking strategy detailed in Algorithm 1.
where 1(-) is the indicator function, Fi,j and Vijj are the reconstructed value and original value of j-th sensor in &, and x; respectively, and M..j j is the element in the i-th row and the j-th column of M. Thus, we can pre-train the sEMG Intrinsic Pattern Capture via the above strategy to obtain well well-initializze model parameters for the downstream task. In practice, we empirically set the masking proportion r as 0. 15 and the average length of masked segments as 3. The pre-training procedure is illustrated in Figure (a).
Specifically, given a transformed signal embedding se- quence X = [x1; X2..., xt], instead of adding masks on the sequence in terms of time steps like BERT, we add sensor- wise masks for the signal sequence of each sensor similar with (Zerveas et al., 2021), which can encourage the model to learn more fine-grained temporal context dependency the signal sequence of multiple electrodes. For the signal se- quence of the i-th sensor, formulated as [T1,,, 22,6 ...; xt,,,, i., the i-th column of X, we generate a binary mask vec- tor m, E IR' where average r radio of elements in m; should be 0.15. Randomly generating m, may cause a lot of isolated-masked signals, meaning one masked signal whose adjacent signals unmasked. However, single signal can be easily predicted by its immediately preceding or suc- ceeding signals, making self- supervised learning easy to fit on ineffective patterns and poor for learning temporal se- mantic information. Considering this, we introduce a more complex masking strategy that aims to generate multiple masked segments on the sequence with an average length 4n. which means m, is composed of contiguous masked segments and unmasked segments. The length of masked segments follows a geometric distribution with mean Lm and the length of unmasked segments follows geometric distribution with mean (,.. Also, 6m/44 = r/(1- - r) so that the number of masked elements would follow the proportion r. While Miao et al. (2022) inspired our use of geometric distribution for segment lengths, we extend this concept by applying the masking strategy in sensor-wise manner and employing different sequence reconstruction methods.
Sensor-wise Mask:
Each electrode has distinct conditions (e.g., moisture level, coating thickness), resulting in varying signal quality. In real-world scenarios, many types of noise are sensor-specific (e.g., signal loss from certain electrodes or poor electrode contact).
Contiguous Masked Segments: The primary frequency range of sEMG signals is 20-500 Hz, while dataset sampling rates are typically around 2000 Hz. This results in similar values for adjacent sampling points. Contiguous masking prevents the model from relying on adjacent sampling points to predict the masked values.
The Length of Masked Segments Follows a Geometric Dis- tribution: The effective frequency range of sEMG signals varies within 20-500 Hz. Frequency changes manifest as variations in the number of effective sampling points in the time domain. Using dynamic-length masked segments bet- ter captures these variations. Geometric distribution aligns with the characteristics of electrode distribution and the frequency dynamics of sEMG signals.
4.3. Long-term and Short-term Decoding
Based on the pre-trained sEMG Intrinsic Pattern Capture, we develop two decoder heads to extract long-term and short- term dependency on the signal sequences, respectively. Intu- itively, the long-term and short-term information on signal sequences is significant in the gesture recognition problem. Long-term information refers to the global context of an sEMG sequence, which provides a signal's overall struc- ture to help interpret the gesture. Short-term information refers to the movement signal in short time interval of the whole sequence, which can provide specific local character- istics for accurate recognition when the overall structures of sEMGs are ambiguous. For example, distinguishing be- tween Index Finger Extension (IFE) and Middle Extension (ME) movements requires a closer examination of the local signal changes in sEMG, whereas differentiating gestures with large variations, such hand gestures and wrist ges- tures, necessitates focus on the global sEMG information.
Then, we can mask the input sEMG signal sequence X by X X 3 M, where O is elementwise multiplication and X is the masked input. With the proposed Transformer-basee
x
sEMG signal encoder, we can obtain the output (L) (x(1))
, 2/ ,..,, x.")) For self-supervised learning, we add a linear layer on the top of masked output to reconstruct each sEMG signal x2(4 as X, E R", the reconstructed sEMG signal in the i-th time step generated from the masked input. Then, we minimize the Mean Squared Error (MSE) of the reconstructed signals and original signals on the masked
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Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in sEMG Analysis
4.3.1. PRESERVING LONG-TERM SEMG SIGNAL
4.3.3. FUSION
X2 x 1 of a sEMG signal sequence, we first build long-term decoder to extract the long-term dependency the complete output.
Given the hidden output X(*) x [t) (1)
Obtained the long-term embeddings H' and the short-term embeddings H of an sEMG signal sequence, we first con- catenate them in terms of the hidden dimension, then in- troduce 1-D convolution to summarize the 1-step sEMG embedding sequence into the final sEMG representation, which is fed into a Feed Forward Layer with a Sigmoid Layer to obtain the final classification probability of which gesture the sEMG belonging to, which can be written as: h u² [H' H"] and y (FC(()), where h E I2ah is the final sEMG representation, u R' is the weight parameters to weighted sum up the sEMG embeddings in each time steps, FC(() is two-layer fully connected layer, a--) is the activation function, and y E RC is the output classification probability of the sEMG signals.
Specifically, the long-term decoder is defined as a multi- head self-attention layer.
The global context signal information is collected to the embeddings of / timesteps with different attention weights through the self-attention layer. The detailed equation is shown in Equation 4a in the appendix.
4.3.2. PRESERVING SHORT-TERM SEMG SIGNAL
We introduce the local context information within a short time interval to extract the short-term dependency on the signal outputs. Similarly, we stack multiple attention heads and calculate the attention of context signals to sum them into the final representations. The difference is we only calculate the attention of its nearest w context for each time step. Specifi- cally, we can rewrite the Attention()) in Equation 4b as:
self-attention layer to model
4.4. Asymmetrie Optimization
Following common practice in multi-label classification, we decompose gesture recognition into series of binary classification tasks. However, two key challenges arise under this formulation. First, due to temporal instability in the sampled signals, the sEMG sequences may exhibit significant sample-wise bias. Some samples with strong signals are easily predicted, while others with ambiguous or noisy signals are much harder classify. Second, the sEMG dataset contains 17 gesture classes with balanced sample counts per class. However, for each binary task, the number of negative samples significantly exceeds that of positives. This class imbalance can suppress gradients from positive instances during training, degrading model performance (Liu et al., 2021a).
Q.K.TT
Attentions (Q, V)- = Softmax
V
Vh
1=1
(2a)
K
liml Unfold(K, w),
(2b)
[Vin 1i=l = Unfold(V, w);
(2c)
where w is the sliding windows size, Q; E R as the i-th query is the i-th row of Q, K." E Rwxh and V E Rex are the keys and values in a window around the i-th query. We utilize the Unf operation for the key and value matrix to generate the sliding windows for each timestep. Note that we omit the index of attention head in the above equation to avoid confusion.
To address these issues, we adopt the Asymmetric Loss (Rid- nik et al., 2021) for gesture classification. This loss, vari- ant of focal loss. (1) applies focusing parameters to down- weight easy samples and emphasize harder ones; and (2) introduces asymmetric focusing and probability shifting to reduce the dominance of abundant easy negatives while pre- serving the contribution of positives. We define the loss function as follows:
Thus, by stacking multiple sets of parameters in Attentions()) to constitute different attention heads, we can obtain the short-term sEMG embeddings H E gixh by H MultiHeadg (x Using sliding windows, each row in H' preserves the local context information of the corresponding timestep, representing the movement from the past 2/2 timesteps to the next w timesteps.
N
LSEET -
Vis (1- "is)"* log (iis))
1=1
+ (1 Di.s) (Tig)) log (1 - Vi.j)
mm
(3)
In contrast to Focal Transformer (Yang et al., 2021), which processes long- and short-term features sequentially, we handle them in parallel. While Zhang et al. (2023b) adopts patch-based segmentation, our method uses dlding-windowv attention to enable fine-grained feature extraction and bet- ter sensitivity to boundary variations. Zhu et al. (2021) combine long-range low-rank attention with short-range diding--indoww attention via unified key/value sets. In com- parison, our model uses two decoupled heads to separately capture long- and short-term dependencies.
and T.")) max (Tig - m, 0), where yay and Tig is the ground-truth and probability of the i-th sEMG signal se- quence belonging to the j-th gesture. (1- "r,)"" and (TT) are two terms to make the weights of hard pre- dicted samples bigger than those easily predicted samples, y+, and 7 are two focusing parameters and y+ > y lead to asymmetric focusing that help the optimization pay more focus on positive samples of each class. D.3 is the shifted
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Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in sEMG Analysis
Table 1:
The table presents the accuracy (ACC, %) and standard deviation (STD) of various models evaluated on Single-finger, Multi-finger, Wrist, Rest, and Overall gesture categories. The proposed STET model shows the highest performance across all categories.
Single-finger STD
Multi-finger
Wrist
Rest
Overall
Model
ACC
ACC
STD
ACC
STD
ACC
STD
ACC
STD
Asif et al. (Asif et al., 2020)
83.44
0.02
83.58
0.01
89.40
0.01
90.86
0.01
85.34
0.01
TCN (Tsinganos et al., 2019)
78.78
0.02
79.10
0.02
87.27
0.01
88.57
0.02
81.50
0.02
GRU (Chen et al., 2021)
84.45
0.02
84.88
0.01
90.06
0.01
89.42
0.02
86.30
0.02
TEMGNet (Rahimian al., 2021)
77.70
0.02
74.00
0.03
84.04
0.02
87.46
0.01
78.02
0.02
Zerveas et al. (Zerveas et al., 2021)
78.45
0.02
77.20
0.02
87.28
0.02
86.76
0.02
80.43
0.02
Informer (Zhou et al., 2021)
86.88
0.02
86.54
0.02
91.90
0.01
83.56
0.02
87.71
0.02
LST-EMG-Net (Zhang et al., 2023b)
87.21
0.01
83.16
0.01
88.36
0.02
82.52
0.02
85.31
0.02
TEMGNe++STEM (ours)
84.57
0.02
81.23
0.02
88.12
0.02
88.74
0.01
84.07
0.02
Informer+STEM (ours)
87.42
0.02
88.39
0.02
92.07
0.01
90.33
0.01
89.14
0.02
STET (ours)
88.27
0.01
89.93
0.02
93.77
0.01
95.33
0.01
90.76
0.01
probability and is shifting margin. The probability shift- ing for negative samples encourages the optimizer to further reduce their contribution.
Average curvature (s)) of all points for each joint is used measure the smoothness of an estimated curve. A smaller k indicates smoother curve. Details above are covered in appendix Appendix G.3.
5. Experiment
5.2. Comparison with Baselines
5.1. Settings
We evaluate the accuracy (ACC) and standard deviation (STD) of our proposed STET in comparison with existing sEMG-based gesture recognition methods. Specifically, we train the model on the GRABMyo dataset (Pradhan et al., 2022), with data processing details provided in Sec- tion 3.1. Classification results are separately reported for single-finger gestures, multi-finger gestures, wrist gestures, rest, and the overall performance. The ratio of the training set to the test set for each gesture is 5 to 2. The experiment results are shown in Table
Implementation Details STET is implemented in PyTorch (Paszke al., 2019) and is trained using one RTX 3090 GPU. During training, we use the RAdam (Liu et al., 2019), which is theoretically sound variant of the Adam optimizer with a weight decay of le-3. For classification tasks, we conduct user-specific pretraining on the GRABMyo dataset, and for regression tasks, we carry out user-specific pretraining on the NinaPro DB2 dataset. In the decoder, we use two layers of full attention in the long-term decoder and two layers of sliding window attention in the short-term decoder. The short-term decoder's 's window size is 41 and 21, and the window's 's move step is set to 1. In both the pre-training and fine-tuning periods, we set the batch size to 16 and set drop out to 0.2.
The experimental results show that STET consistently achieves the best performance across all four gesture cate- gories as well as the overall dataset. In particular, STET and Zerveas et al. (2021) both employ Transformer-basee en- coders. However, in the decoder stage, STET integrates both short-term and long-tern decoders, in contrast the fully connected layers used in Zerveas et al. (2021). As result, the overall accuracy is improved from 80.43% to 90.76%. This improvement stems from the proposed long-term and short-term decoupling module, which captures both global and fine- ame-grained dependencies in sEMG signals, leading to more informative representations.
5. EVALUATION METRICS
Following the prior works (Guo et al., 2021; Wang et al., 2020a; Chen et al., 2021; Rahimian et al., 2021), we choose the below metrics to evaluate the model's performance: Pearson Correlation Coefficient (CC) is widely used measure of the linear relationship between two variables. It ranges from- 11 to 1, where a larger CC value indicates greater similarity between the predicted and estimated joint angles curve, indicating improved estimation.
Among the transformer- based methods, Informer and STET performed best, with accuracy rates of 87.71 % and 90.76%, respectively. Informer relies heavily on max pooling layers to aggregate features, leading to the relative weakness in extracting some short-term features. STET enhances accu- racy and stability by strengthening the short-term feature extraction. The improvement is remarkable on the Rest ges- tures, where the accuracy improves from 83.56% 95.33%. Furthermore, after incorporating our designed short-term en- coder into Informer, its accuracy rate increased from 87.71
Root Mean Square Error (RMSE) is common metric for evaluating the deviation between predicted and observed values. As the range of fluctuations in the curves of different joint angles can vary significantly, it is difficult to fairly evaluate the performance of models using RMSE alone. Normalization of RMSE addresses this issue, resulting in the Normalized RMSE (NRMSE).
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Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in sEMG Analysis
0.79
0.75
0.75
Accorsey
0.7
0.75
Accurscy
0.7
0.68
0.71
0.65
Fuse decoder
Fuse decoder
Short decoder
Long term decoder
0.67
Short term decoder
Long term diecoder
Short term decoder
Long term decoder
0.35
Transformer
0.63
Transformer
0.55
Transformer
Additive
Multiplicativv
Drup Point
Figure 3: Accuracy versus Noise Intensity Curve. The accuracy of different decoders (Fuse, Long term, Short term, and Transformer) under varying levels of Additive Gaussian Noise, Gaussian Multiplicative Noise, and Drop Point Noise.
Table 2: Ablation study results. EIPC: sEMG Intrinsic Pattern Capture; LT: Long-Term decnder; ST: Short-Term decoder; CEL: Cross Entropy Loss; ASL. Asymmetric Loss.
tary roles in sEMG signal recognition, and that short-term information should not be neglected. Most notably, when our fusion module used to combine long-term and short- term features, accuracy further increases by 2.46%. This result confirms that the two decoders are complementary and that explicitly enhancing short-term features on top of long-term modeling is beneficial. Finally, apply Asym- metric Loss (ASL) to emphasize difficult samples during training. Compared to using Cross Cross-Entropy Loss (CEL), this leads to 0.73% improvement in accuracy, demonstrating the effectiveness ASL in improving model robustness.
Transformer
EIPC
LT ST Fusing CEL ASL
ACC (%)
85.73
86.33
88.02
87.72
V
89.37
/
89.42
/
90.54
5.4. Robustness Analysis
to 89.14%, and the classification accuracy for Rest gestures improved from 83.56% to 90.33%.
To verify the model's 's robustness, we only used data collected in the lab train the model and added dif- ferent types of noise (Additive and Multiplicative Gaussian noise and signal loss) during validation to simulate complex scenarios that might be encountered in real situations.
The Rest gesture, representing the idle state of devices such as interactive bracelets, is the most frequent gesture class in the dataset. As result, the stability of its prediction is crucial for reliable gesture recognition. STET achieves the highest and most stable performance on the Rest cate- gory compared to all baseline methods, demonstrating its robustness in handling frequent and critical gesture states.
Additive noise typically refers thermal noise added the original signal. This type of noise exists regardless of the presence of the original signal and is often considered the background noise of the system in sEMG acquisition. Multiplicative noise is generally caused by channel insta- bility and has a multiplicative relationship with the original signal. Also, we simulated signal loss during transmission by randomly setting a portion of the signals to zero.
5.3. Ablation Studies
To validate the effects of the unsupervised sEMG Intrinsic Pattern Capture (EIPC), Long-term decoder, Short-term decoder, Fuse strategy, and loss function. We designed variants of STET and reported their results in Table 2.
Figure 3 illustrates the influence exerted by three distinctive noise categories, namely additive noise, multiplicative noise, and signal loss, on the accuracy of the proposed model. The model using only the short-term decoder is less affected by noise than the long-term version. This relative robustness of the short-term decoder is potentially attributable to its unique capability to mitigate the global impact of noise by virtue of a sliding window multiple-sampling scheme, which effectively confines the sphere of noise impact. The model that integrates both long-term and short-term characteristics persistently outperforms models that rely on only one. This highlights the significant effectiveness of the integrating process in dealing with noise-induced interference.
First, observe that with the introduction of unsuper- vised EIPC, the accuracy of both the Transformer and STET improves by 0.60% and 1. 1.12%, respectively, compared to training from scratch. This suggests that EIPC enables the model to capture additional features from the data-such as the inherent variability in sith-without the need for new samples or additional annotations. Importantly, this process avoids reliance on external data, thereby preserving user privacy during data acquisition and processing. Replac- ing the fully connected layer in the Transformer's decoder with either the long-term decoder or the short-term decoder improves performance by 1.16% and 1.39%, respectively. The similar effectiveness of both variants indicates that long- term and short-term features play distinct and complemen-
As depicted in Table 3, it is evident that both Transformer and Informer models demonstrate a notable enhancement
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Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in sEMG Analysis
Table 3: Drop rates of accuracy calculated by drop rate = (ACCraw ACCnoise) /ACCrww. AG: Additive Gaussian noise,
Table 4: Model performance comparison. PCC: Pearson Correla-
tion Coefficient, NRMSE: Normalized Root Mean Squared Error.
MG: Multiplicative Gaussian noise.
Model PCC
NRMSE +
R
Epoch Time (s) 1
Backbone In STET
AG Noise
MG Noise
Signal Loss
LSTM TCN
0.779
0.096
0.581
26.36
0.833
0,088
1.533
3.62
Transformer
No
25%
16%
14%
BERT
0.867
0,077
1.571
4.95
Transformer
Yes
10%
10%
8%
SBERT-OHME
0,869
0.076
0.532
4.96
STET (Ours)
0.877
0.073
0.522
6.83
Informer
No
11%
9%
26%
Informer
Yes
9%
8%
17%
term embedding. Meanwhile, long-term embedding can distinguish LP and TIFE gestures very well, but short-term embedding will confuse them. As shown in Figure 4(c), af- ter the fusion of the two types of features, the classification interface is wider, and the confusion points are significantly reduced, which indicates that the fusion module can effec- tively complement the strengths of the two types of features.
THE
Figure 4: Visualization of (a) the long-term sEMG embeddings, (b) the short-term sEMG embeddings, and (e) the fused sEMG embeddings for gesture recognition. Note that we color cach sample by its classes.
5.6. Regression: Hand Joint Angles Prediction
STET can conveniently handle regression tasks by chang- ing the loss function to mean squared error (MSE) loss. Continuous motion estimation extracts continuous motion information, such as joint angles and torques, from sEMG signals. Since continuous motion estimation requires out- putting subtle movement variations at each time instant, the local signal variations are particularly important for this type of estimation. In this section, we have re-selected the most competitive models known for sEMG-based joint an- gle prediction as the baseline and tested the performance of STET on the regression task of predicting the main 10 joint angles for fingers using the Ninapro DB2 (Atzori et al., 2014) dataset. As shown in Table 4, STET achieved the best performance in PCC, NRMSE, and K, indicating that the joint angle curve predicted by STET is more in line with the real curve and has less abnormal fluctuations, which will significantly improve the user's interactive experience. In terms of training time, due to the addition of the short-term decoder, its training speed is slightly slower than BERT but still within an acceptable range.
in noise resistance when they adopt the design from STET. Our short-term enhancement can be easily extended to other models. It is evident that both Transformer and Informer models demonstrate a notable enhancement in noise resis- tance when their decoders are replaced with the design from STET. Specifically, when comparing the drop rates of accu- racy due different noise types, calculated by drop rate: Transformer: Without the STET framework, it experienced a drop of 25% under additive Gaussian noise, 16% under multiplicative Gaussian noise, and 14% with signal loss. However, when integrated into the STET framework, these drops were reduced to 10% for both additive and multiplica- tive Gaussian noise and 8% for signal loss.
Informer: Without the STET framework, it showed a drop of 11% due to additive Gaussian noise, 9% due to multi- plicative Gaussian noise, and significant 26% with signal loss. With the STET design, these rates improved 9% for additive Gaussian noise, 8% for multiplicative Gaussian noise, and 17% for signal loss.
6. Conclusion
5.5. Visualizations
Current sEMG- based gesture recognition models usually fail to handle various noisy and distinguish similar gestures, especially in non-laboratory settings. In this paper, we found using short-term information and self-supervise EIPC mit- igates this issue. Therefore, we proposed STEM to capture local signal changes and enhance noise resistance. STEM is easily deployable and serves as a plug-in that can potentially be applied to most time series deep learning models. Ac- cording to our experimental results, our method significantly improved performance for both classification and regression tasks in sEMG, and the model's 's ability to resist signal loss, Gaussian additive noise, and Gaussian multiplicative noise was clearly improved. This will further drive the practical application of sEMG in VR, AR, and other human- computer interaction scenarios.
To demonstrate the distinction of different gestures, first obtained STET's long-term, short-term, and fuse embed- dings. The embeddings with dimensions (N, T, H) were then flattened to (N, T * H) and separately projected in 2D by t-SNE, the result shown in Figure 4. We colored each node by category for the illustration. As shown in Figure 4, The classification boundary generated by the long-term fea- ture and the short-term feature is a significant difference, indicating that the long-term and short-term features are capable of recognizing different types of gestures. This further suggests that the two features are complementary in data representation. For example, short-term embedding can distinguish TA gestures from TIFO gestures very well, but TA gestures and TIFO gestures will be confused in long-
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Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in sEMG Analysis
Software
17468108. doi: 10.10166j..bbp:22112222..0 URL https:
The code is available httpp:///githue conn/guoweiyu
/ //oii.or//// / 1016 bspc. 2019. 02.011.
short-term-se semg.
Borbély, B. J. and Szolgay, P. Real-time inverse kinematics for the upper limb: model-based algorithm using seg- ment orientations. Biomedical engineering online, 16(1): 1-29, 2017.
Acknowledgements
This work was supported in part by the National Key Research and Development Program of China (Grant No.2023YFF0725001), in part by the National Natural Science Foundation of China (Grant No. 92370204, 62306255), in part by the Guangdong Basic and Applied Basic Research Foundation (Grant N0.202331151112005,, 2024A151501 1839), in part by Guangzhou--HKUSTCCZZ Joint Funding Program (Grant No. No.2023A0310008), the Fundamental Research Project of Guangzhou (No.
Chen, R.. Chen, Y., Guo, W., Chen, C., Wang, Z., and Yang, Y. semg-based gesture recognition using gru with strong robustness against forearm posture. In 2021 IEEE International Conference on Real-time Computing and Robotics (RCAR), pp. 275-280, IEEE, 2021.
Chen, X., Guo, W., Lin, C.,, Jiang, N., and Su, J. Cross- subject lifelong learning for continuous estimation from surface electromyographic signal. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2024.
2024A04J4233), Education Bureau of Guangzhou Munici- pality.
Impact Statement
Clancy, E. A., Liu, L., Liu, P., and Moyer, D. V. Identifi- cation of constant-pone posttureep ung-torque relationship about the elbow using nonlinear dynamic models. IEEE Trans- actions on Biomedical Engineering, 59:205-212, 2012. ISSN 00189294, doi: 10. 10.1109/TBME.201 1.210023....
This paper presents work whose goal is to advance the field of Machine Learning, particularly in sEMG-hased gesture recognition. We believe our work has several potential positive societal consequences, such enabling more in- tuitive and robust control in human-computer interaction, especially for VR/AR applications, and improving assistive technologies like prosthetic hands through more stable and precise motion intention prediction. By enhancing noise resilience, this research can lead to wider adoption and more practical applications of sEMG-based systems in real-world environments, facilitating new developments in fields requir- ing fine-gra tiai-grained motion analysis and control.
Dai, L., Xu, Y., Ye, J., Liu, H., and Xiong, H. Seper: Measure retrieval utility through the lens of semantic perplexity reduction. In The Thirteenth International Conference on Learning Representations.
Du, Y., Wong, Y., Jin, W., Wei, W., Hu, Y., Kankanhalli, M. S., and Geng, W. Semi-supervised learning for surface emg-based gesture recognition. In IJCAI, pp. 1624-1630, 2017.
References
Guo, W., Ma, C., Wang, Z., Zhang, H., Farina, D., Jiang, N., and Lin, C. Long exposure convolutional memory network for accurate estimation of finger kinematics from surface electromyographic signals. Journal of Neural Engineering, 18, 2021. ISSN 17412552. doi: 10.1088/ 1741-2552/abd44
Asif, A. R., Waris, A., Gilani, S. O., Jamil, M., Ashraf, H., Shafique, M., and Niazi, K. Performance evaluation of convolutional neural network for hand gesture recognition using emg. Sensors, 1642):644, 2020.
Atzori, M., Gijsberts, A., Castellini, C.. Caputo, B., Hager, A. M., Elsig, S., Giatsidis, G., Bassetto, F., and Müller, H. Electromyography data for non-invasive naturally- controlled robotic hand prostheses. Scientific data, 1(1):
Guo, W., Jiang, N., Farina, D., Su, J., Wang. Z., Lin, C., and Xiong, H. Multi-attention feature fusion network for accurate estimation of finger kinematics from sur- face electromy signals. IEEE Transactions on Human-Machine Systems, 53(3)=512-519, 2023.
1-13, 2014.
Becker, V., Oldrati, P., Barrios, I.., and Sörös, G. Touch- sense: classifying finger touches and measuring their force with electromyography armband. In Proceedings of the 2018 ACM International Symposium Wearable Computers, pp. 1 1-8, 2018
Guo, W., Sun, Y., Xu, Y., Qiao, Z., Yang. Y., and Xiong, H. Spgesture: Source-fres free domain Nomain-adaptive semg-based gesture recognition with jaccard attentive spiking neural network. arXiv preprint arXiv:2405. 14398, 2024.
Hashemi, J., Morin, E.,, Mousavi, P., Mountjoy, K., and Hashtrudi-Zaad K. Emg-force modeling using parallel cascade identification. Journal of Electromyography and Kinesiology, 22:469-4777 2012. ISSN 1050-6411. doi: 10.1006G
Bi, L., Feleke, A., and Guan, C. A review on based motor intention prediction of continuous human upper limb motion for human-robot collaboration. Biomedical Signal Processing and Control, 51:113-127, 2019. ISSN

---

Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in sEMG Analysis
Jiang, X., Guo, W., Cui, Z.,, Lin, C., and Su, J. Decompo- sition of high-density semg signals: Extracting multiple spikes from single time windows. Biomedical Signal Processing and Control, 107:107771,202
Miao, Q Xu, C., Zhan, J., Zhu, D., and Wu, C. An un- supervised short-and long-term mask representation for multivariate time series anomaly detection. In Interna- tional Conference on Neural Information Processing, PP. 504-516. Springer, 2022.
Koike, Y. and Kawato, M. Estimation of dynamic joint torques and trajectory formation from surface electromyo- graphy signals using neural network model. Biological cybernetics, 73(4):29) (((4):911-30)) 1995.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury. J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L.,, et al. Pytorch: An imperative style, perf-nerf deep learning library. Advances in neural information processing systems, 32, 2019.
Koirala, K., Dasog, M., Liu, P., and Clancy, E. A. Using the electromyogram to anticipate torques about the elbow. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23(3):96-402,, 2015, doi: 10.1109/TNSRE. 2014.2331686.
Pradhan, A., He, J., and Jiang, N. Multi-day dataset of fore- and wrist electromyogram for hand gesture recogni- tion and biometrics. Scientific Data, 91)::1110, 2022.
Li, W. Shi, P., and Yu, H. Gesture recognition using sur- face electromyography and deep learning for prostheses hand: state-of-the-art, challenges, and future. Frontiers in neuroscience, 15:621885, 2021.
Rahimian, E., Zabihi, S., Atashzar, S. F., Asif, A., and Mo- hammadi, A. Xceptiontime: Independent time-window xceptiontime architecture for hand gesture classification. ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1304-1308. IEEE, 2020.
Lin, C., Chen, X., Guo, W., Jiang, N., Farina, D., and Su, J. A bert based method for continuous estimation of cross-subject hand kinematics from surface electromyo- graphic signals. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022.
Rahimian, E., Zabihi, S., Asif, A., Farina, D., Atashzar, S. F., and Mohammadi, A. Temgnet: Deep transformer- based decoding of upperlimb semg for hand gestures recognition. arXiv preprint arXiv:21099 2te///99.222 /2379, 2021.
Lin, C., Zhao, C., Zhang, J., Chen, C., Jiang, N., Farina, D., and Guo, W. Continuous estimation of hand kinemat- ies from electromyographic signals based on power-and time-efficiee transformer deep learning network. IEEE Transactions on Neural Systems and Rehabilitation Engi- neering, 2024.
Recommendation, C. Pulse code modulation (pem) of voice frequencies. In ITU, 1988.
Ridnik, T., Ben-Baruch, E., Zamir, N., Noy, A.. Fried- man, I., Protter, M., and Zelnik-Manor, L. Asymmetric loss for multi-label classification. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 82-91, 2021.
Liu, L., Jiang, H., He, P., Chen, W., Liu, X., Gao, J., and Han, On the variance of the adaptive learning rate and beyond. arXiv preprint arXiv: 03265, 2019.
Sun, Y., Xu, C., Li, G., Xu, W., Kong, J., Jiang, D., Tao, B., and Chen, D. Intelligent human computer interac- tion based on non redundant emg signal. Alexandria Engineering Journal, 59(3): 1199):11491117 2020.
Liu, P., Liu, L., and Clancy, E. A. Influence of joint angle on emg-torque model during constant-posture torque- varying contractions. IEEE Transactions on Neural Sys- tems and Rehabilitation Engineering, 23(6)::033-1044 2015. doi: 10.1 111.11//NNEE..1155555557777
Tsinganos, P., Cornelis, B., Cornelis, J., Jansen, B., and Skodras, A. Improved gesture recognition based on semg signals and ten. In ICASSP 2019 2019 IEEE International Conference on Acoustics, Speech and Sig- nal Processing (ICASSP), pp. 1169-1173, 2019. doi: 10. D0.100/CASSP.201....om 8683239.
Liu, Y., Ao, X., Qin, Z., Chi, J., Feng. J., Yang, H., and He, Q. Pick and choose: a gnn-based imbalanced learning approach for fraud detection. In Proceedings of the Web Conference 202/, pp. 3168-3177, 2021a.
Liu, Y., Lin, C., and Li, Z. Wr-hand: Wearable armband can track user's hand. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 5(3):1- 27, 2021b.
Wang, C., Guo, W., Zhang, H., Guo, L., Huang, C., and Lin, C. semg- continuous estimation of grasp move- ments by long-short term memory network. Biomedical Signal Processing and Control, 59: 2020a. ISSN 17468108. doi: 10. 10.10016/j (s0.....9p.2201..11777 101774. URL
Liu, Y., Zhang, S., and Gowda, M. Neuropose: 3d hand pose tracking using emg wearables. In Proceedings of the Web Conference 2021, pp. 1471-1482, 2021c.
http://210.7e 76. 142: MSVXTLLU@JJBB10011111 1016/ bspc. 2019 101774.
10

---

Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in sEMG Analysis
Wang, C., Zhu, H., Zhu, C., Qin, C., and Xiong, H. Setrank: A setwise bayesian approach for collaborative ranking from implicit feedback. In Proceedings of the aaai confer- ence on artificial intelligence, volume 34, pp. 6127-6136, 2020b.
Zhang, W., Zhao, T., Zhang, and Wang. Y. Lst-emg-net: Long short-term transformer feature fusion network for semg gesture recognition. Frontiers in Neurorobotics, 17: 1 1127338, 2023b.
Zhao, Y., Zhang, Z., Li, Z., Yang, Z.. Dehghani-Sanij A. A.. and Xie, S. An emg-driven musculoskeletal model for estimating continuous wrist motion. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28 (12)3113333100 2020.
Wang, L. and Buchanan, T. Prediction of joint moments using neural network model of muscle activations from emg signals. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 10(1):30-37, 2002. doi: 10. 1109/TNSREE2202.10225555
Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., and Zhang, W. Informer: Beyond efficient transformer for long sequence time-series forecasting, In Proceedings of the AAAI Conference on Artificial Intelligence, pp. 11106-11115, 2021.
Xiong, D., Zhang, D., Zhao, X., and Zhao, Y. Deep learning for emg-based human-machine interaction: A review. IEEE/CAA Journal of Automatica Sinica, 8(3):512-533,
2021. doi: 10. 10009/15.2221.1003333
Zhu, C., Ping, W., Xiao, C., Shoeybi, M., Goldstein, T., Anandkumar, A., and Catanzaro, B. Long-short trans- former: Efficient transformers for language and vision. Advances neural information processing systems, 34: 17723-17736, 2021.
Xu, Y., Liu, A., Hu, X., Wen, L., and Xiong, H. Mark your Ilm: Detecting the misuse of open-source large language models via watermarking. In The 1st Workshop on GenAl Watermarking.
Yang, J., Li, C., Zhang, P., Dai, X., Xiao, B., Yuan, L., and Gao, J. Focal self-attention for local-global interactions in vision transformers. arXiv preprint arXiv:2107.00641 2021.
Yao, S., Zhuang. Y., Li, Z., and Song, R. Adaptive ad- mittance control for ankle exoskeleton using emg- driven musculoskeletal model. Frontiers in neurorobotics, 12:16, 2018.
Zerveas, G., Jayaraman, S., Patel, D., Bhamidipaty, A., and Eickhoff, C. A transformer-bow based framework for multi- variate time series representation learning, In Proceed- ings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 2114-2124, 2021.
Zhang, H., Sun, Y., Guo, W., Liu, Y., Lu, H., Lin, X., and Xiong, H. Interactive interior design recommendation via coarse-to-fine multimodal reinforcement learning. In Proceedings of the 31st ACM International Conference on Multimedia, pp. 6472-6480, 2023a.
Zhang. Q., Zhu, H.. Sun, Y., Liu, H., Zhuang, F., and Xiong, H. Talent demand forecasting with attentive neural se- quential model. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp- 3906-3916, 2021.
Zhang, Q., Jing, J., Wang, D., and Zhao, R. Wearsign: Push- ing the limit of sign language translation using inertial and emg wearables. Proceedings of the ACM on Interac- tive, Mobile, Wearable and Ubiquitous Technologies, 6 (1):1-27, 2022.

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Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in sEMG Analysis
Part I Appendix
A. The details of methods
A.1. The details of sEMG Signal Masking
The algorithm of sEMG Signal Masking shown in The algorithm generates a mask matrix M, where each element m.y indicates whether the j-th sampling point of the i-th sensor is masked.
Algorithm 1
The Algorithm of sEMG Signal Masking
Require: The length of the input signal sequence t
Require: The number of signal sensors e
Require: The average length of masked segments Im
Require: The masked ratio r
Ensure: The mask matrix M
1: for = ,1,..,,... do
2: m, f [True]'s % Initialize all elements to True
3: Pm 1 % Probability of ending a masked segment
i..
4:
Pu < Pm- % Probability of ending unmasked segment
5:
p Pm.PPul
6: state + Bool Brandoom (0, (0, 1) > r) Initialize first state
7: for j ,,....t do
8: m.j < state
9: if random(0, 1) p[state] then
state < ssatt {Flip the state]
11: end if
12: end for
13: end for
14: Return M + (mm:li-)*
The choice of a sensor-wise masking strategy aligns well with the practical scenarios of sEMG noise, where signal disturbances often occur the level of individual sensors. For instance, signal loss from specific electrode electrodes become detached misaligned frequently affects specific sensor data. By employing sensor-wis sisorrwwieeee masking, approach effectively mimics these real-world noise conditions. Furthermore, it encompasses scenarios of multi-sensor signal loss, broadening the model's generalizability across diverse noise conditions. This strategy encourages the model to focus on learning the intrinsic patterns of sEMG signals from unmasked sensors, enhancing its ability to infer missing data. Unlike other masking techniques, sensor-wise masking does not overly depend on spatial-temporal uniformity across sensors, which is erucial given the localized nature of many sEMG disturbances. This robustness ensures better generalization across various signal conditions, demonstrated by our model's improved performance in handling noisy and partial signals. By addressing practical noise issues systematically, the proposed approach enhances the model's 's resilience in real-world applications.
where
A.2. Role of unsupervised pretraining and noise robustness
Our sEMG Signal Masking strategy aims to capture intrinsic variability in the signal rather than explicitly removing noise. This improves the model's robustness by:
Preventing overfitting on spurious patterns in supervised training.
Enabling better feature learning from unlabeled data.
As shown in Table 3, this approach reduced performance degradation under various noise conditions (e.g., additive Gaussian noise impact decreased from 25% to 10%).To further understand the contribution of the pretrain with sEMG Signal Masking strategy, we conducted additional experiments to evaluate its impact on the model's 's robustness to various noise types. Specifically, we removed the short-term and long-term enhancement modules and assessed the backbone model (Transformer) under conditions with and without pretraining using the sEMG Signal Masking strategy. Other experimental
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Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in sEMG Analysis
conditions, such ASL loss, remain unchanged. Due time constraints, this experiment was conducted data from only three subjects. The results are summarized below:
A.3. The details of experiment on Table 2
In this experiment, we utilized transformers and informers as the backhone network, employing the same parameters as in Table The STET framework incorporates a pretraining strategy with sEMG masks and STEM on top the backbone. In contrast, the No STET framework uses a standard pretraining with a general mask, employing a standard masking approach with fixed segment lengths of (aligning with the average length our method), vensor-agnostic masking, and a random masking ratio of 15% (consistent with method). Other experimental conditions, such ASL loss, remain unchanged.
Backbone
Pretrain with sEMG Signal Masking
AG Noise (%)
MG Noise (%
Transformer
No
22.00
15.00
Transformer
Yes
15.00
13.00
Table Comparison of performance with and without sEMG Signal Masking strategy for Transformer backbone.
A.4. The definition of long-term encoder
The long-term encoder is defined as follows:
MultiHeadı (x()) = Concat (hi,..., ha) Wo
(4a)
where
{hh d = (Attention (Q. K, Ville 1=0
(4b)
Q.K! Vi,
Attention (Qi, K, V,) = Softmax
(4c)
where Q; x x V.= W! and (W? W.K W d
== Rhxh are parameter matrices and is the number of attention heads. Concat(-) represents the concatenate operation. Wo gddix is the output parameter matrix to transform the concatenated outputs of d attention heads. Then, the long-term sEMG embeddings H' E RRt is obtained by H MultiHeady, ((aliHHaadx(())))))) (X)))).
The linear projection is applied in the long-term enhanced module because this module processes inputs of fixed length due its structural design. We use linear projection here adjust the feature dimensions appropriately. In contrast, the short-term enhanced module operates based sliding window, which means the input length remains consistent and does not require dimensional adjustment. Therefore, linear projection is not needed in the short-term module.
B. The significance of the results
We conducted Friedman and Wilcoxon signed-rank tests to analyze significant differences among subjects with different evaluation methods, and we corrected the P-value using Bonferroni correction. Our model outperformed the other models in Table (P < 0.01). Finally, we would like to emphasize that in sEMG-related HCT applications, even small improvement in accuracy can significantly improve user experience. Therefore, we believe that our proposed model can significantly improve user experience.
B.1. The details of noise
Additive noise typically refers to thermal noise added to the original signal. This type of noise exists regardless of the presence of the original signal and is often considered the background noise of the system in sEMG acquisition. Additive noise can be described as:
Godd(z) a N(x),
(5)
(I - u2²
N(x)
exp
(6)
2n
20
where G(z)add represent the signal with additive noise, a used to adjust the size of the noise. (zx) a normal distribution that simulates background noise. Here, we set O and a to
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Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in sEMG Analysis
Multiplicative noise is generally caused by channel instability and has multiplicative relationship with the original signal. Multiplicative noise used in the experiment can be described as:
Gmul( = x + (x),
(7)
x²2
\10 SNB
Nmu = n
(8)
(x)²
where G(v)add represent the signal with multiplicative noise, SN R stand for the signal to noise ratio. Additionally, we simulated signal loss during transmission by randomly setting portion of the signals zero.
C. Real-World Deployment and human-subject study
Although most previous works, including ours, have primarily tested the performance of gesture detection algorithms offline laboratory datasets (Wang al., 2020a; Chen et al., 2021), recognize the importance of real-world application scenarios. These scenarios often involve complex variables such as electrode movement and muscle state changes, which offline testing does not capture adequately. As shown in Figure 1, we expanded our evaluation to include online performance verification to address this gap. This was achieved by integrating our algorithm with a 3D virtual hand, which was developed using the Unreal engine and controlled through STET decoding of sEMG signals.
Figure 5: STET-based Real-Time Hand Interaction Reconstruction from Wrist sEMG Signals.
To make our system more aligned with daily usage habits, we placed four myoelectric electrodes on both the left and right wrists of users, each with a sampling rate of 2000Hz. The signals were transmitted to the host computer via a WiFi interface for continuous motion estimation. Our online experiment results demonstrated an overall latency of less than 50ms and a Pearson Correlation Coefficient (PCC) of over 0.8. This indicates stable, accurate, and natural interactive experience, surpassing the capabilities of computer vision-based methods regarding energy efficiency and independence from lighting conditions and occlusions.
To enable streaming processing online, we implemented an online buffer mechanism. Specifically, the buffer collects incoming signals in real-time, and every 10ms, the model retrieves the most recent 200ms of signals from the buffer for processing. The architecture used for online testing is identical to the one described in the paper. Furthermore, the model
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Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in sEMG Analysis
has been deployed in commercial sEMG interaction application, where it has undergone testing in VR control scenarios, demonstrating its practical usability.
In our human-subject study, we recruited total of eight healthy participants, comprising an equal gender distribution of four males and four females, all of whom were right- right-handed. Each participant was asked to perform six distinct movements, captured using a standard transformer model and our STET model. Importantly the participants were blinded the model used during their tasks. After engaging in 10-minute gaming session designed test the models, participants identify which model they felt more stable and provided better experience. Remarkably, out of the eight participants, seven preferred the STET model, citing its greater stability and overall performance. This overwhelming preference for the STET model amongst participants highlights its efficacy and potential for real-world applications, reinforcing our findings regarding its superior performance compared to traditional models.
asked
Thunb adliction
finux exazsioo
Indes
Tounh
(4)
Lateni prchension (1P))
Thumb and littl finger oppositon (TL,OO)
Thunb and indos finger oppostion (IIF)
Thank and indox finger extension (TIFF)
Thumb and litle finge extassion (TLFE)
Indox and midéle finger (IMFE)
Hind
Findd closs
(b)
Wrist extension (W1))
Wrist flecon (WF)
Forearmt pronation (PP)
Foceam repination (PS)
(c)
Figure 6: The gestures were used in gesture classification experiment(aa) Single- finger gestures (b) Multi Multi-finger gestures (c) Wrist gestures.
D. The difference from LST-EMG-NET
Our proposed approach exhibits marked deviation from the LST-EMG-Net detailed in (Zhang al., 2023b). In the LST-EMG-Net, the raw sEMG data is pre-segmented into long and short durations prior to the network ingestion. This pre-segmentation could potentially hinder the encoder's capability to grasp the intrinsic patterns of sEMG. In contrast, our method divides the data into short-term and long-term segments during decoding, which we believe is a more effective strategy. Our results support this, with our method achieving an accuracy of 90.8% compared to 85.3% achieved by the LST-EMG-Net model.
E. Noise Analysis Metrics
We have employed two key metrics to analyze the noise distribution quantitatively:
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Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in sEMG Analysis
Signal-to-Noise Ratio (SNR) We analyzed the spectral properties of each sEMG signal recording and measured the SNR (in dB) as the ratio between the power of the signal the power of the noise [1]. The power of the noise was estimated as the power of sEMG recordings during the rest trial (when SEMG acquisition is least susceptible to interference) [1]. The average SNR across all signals in our datasets was 14.565 + 6.385 dB.
Correlation Coefficient of Normality (CCN) This metric was used to analyze amplitude distribution. For static contraction with moderate force, sEMG can be modeled as a filtered, random, white Gaussian noise process [2]. It has been suggested that a test of normality can provide a measure of biosignal quality, where a signal amplitude with non-Gaussian distribution would be considered contaminated. We generated a Gaussian distribution with equal mean and variance to that of the recording [3]. The CCN is defined as the Pearson correlation coefficient between the histogram bin values of the sEMG recording and the normal density function value for the corresponding bins [4]. A value close to indicates a normal distribution. The CCN of all signals in our dataset was 0.975 + 0.041.
In the real-world dataset, noises are complexly and randomly intermingled, making precise quantitative isolation impossible. Therefore, we used SNR and CCN calculations to analyze the overall noise distribution in spectral and amplitude properties. Our measurements of the GrabMyo dataset revealed the following distribution:
Table 6: SNR Distribution in the GrabMyo Dataset
Metric High noise (SNR~ < 10 dB)
Moderate noise (10 dB S SNR S 20 dB) Low noise (SNR > 22 dB)
SNR
28.2%
56.3%
15.5%
Through visual inspection and based on our experience, we identified samples with CCN < 0.93 (11.6% of the total samples) as having severe non-Gaussian signals, which may be affected by transient noise such as motion artifacts electromagnetic interference.
F. Dataset
We conducted the experiments on the Gesture Recognition and Biometrics FlectrooMyogram (GRABMyo) Dataset, the largest known open-source wrist EMG dataset with great potential for developing new generation human-maching interaction based on sEMG. GRABMyo has 43 healthy subjects whose average age is 26.35 + 2.89, and the average forearm length is 25.15 - 1.74 cm (measured as the distance between the olecranon process and the ulnar styloid process). There were 23 male subjects and 20 female subjects, respectively.
Data collection. Place 2 rings of 12 monopolar sEMG electrodes (AM-NO0S/E, Ambu, Denmark) at the wrist position; each ring consists of 6 electrodes. The sample rate was set to 2048Hz. The distance between the center lines of adjacent electrodes is 2cm. To keep the electrode positions consistent across subjects, the position of the first electrode was fixed at the centerline of the elbow crease. The subjects performed 17 gestures of hand and wrist (including a rest period sEMG) according to the prompts on the computer screen. Each gesture was repeated seven times, each lasting 5 seconds. To avoid muscle fatigue, rest 10s between repetitions. In the following experiments, we use five repetitions as the training set and two repetitions as the test set.
Data processing. To avoid muscle fatigue, rest 10s between repetitions. In the following experiments, we use five repetitions as the training set and two repetitions as the test set. Bandpass filtered between Hz and 500 Hz with a gain of 500 was adopted the raw signal. We use the difference between the corresponding electrodes in the two loops as the input signal of our model.
Sizes of Training, Validation, and Test Sets. Table 7 shows the division of the training set, validation set, and test set, which aligns with the common comparison practices in the field. Note that after selecting the parameters using the validation set, the validation set will be merged into the training set for retraining.
Gesture Classification Task: For this task, we used the GRABMyo dataset for both pre-training and fine-tuning. The GRABMyo dataset provides the necessary data for gesture classification, but it does not include joint angle information. Hand Joint Regression Task: Since the GRABMyo dataset lacks joint angle data, we employed the Ninapro DB2 dataset for this task. Both pre-training and fine-tuning were conducted using data exclusively from Ninapro DB2.
Derreann--Deeedenntt Experiments. Our experiments are participant-dependee (user-specific). For each user, we train and evaluate the model using data exclusively from that user. This approach aligns with the objectives of our study, focusing on enhancing the model's 's robustness and performance for individual users without introducing additional data from other
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Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in sEMG Analysis
Task Type
Data Used
Samples per User (except rest)
Classification
Training Set
Five repetitions per gesture per user One repetition randomly selected from the training Two repetitions per gesture per user
42,500 samples
Validation Set
8,500 samples
Test Set
17,000 samples
Regression
Training Set
Four repetitions per gesture per user One repetition randomly selected from the training Two repetitions per gesture per user
34,000 samples
Validation Set
8,500 samples
Test Set
17,000 samples
Table 7: Data Splits for Classification (GRABMyo) and Regression (Ninapro DB2) Tasks
participants during pre-training fine-tuning. This is common practice in sEMG studies when aiming to optimize performance for specific users.
G. Supplementary details of the experimental section
All comparison methods reported in our paper were re-implemented by us. This decision was made because different methods in the literature often use different datasets, select varying subsets of gesture categories, or employ different evaluation protocols, making direct comparisons challenging. By re-implementing these methods under consistent
framework, we ensured that all methods were evaluated:
On the same dataset(s)
Using the same set of gesture categories
Under identical training, validation, and testing conditions
With consistent hyperparameter tuning strategies based on the original papers
G.1. Fairness of Comparison Regarding Pretraining
We want to emphasize that the pretraining in our work is fundamentally different from typical pretraining approaches in fields like NLP. Unlike models that are pre-trained on large external datasets and then fine-tuned on specific tasks, our method performs pretraining without introducing any external data. We use only the user-specific training data for both pretraining and fine-tuning. This means that both our method and the comparison methods are trained and tested on exactly the same data, ensuring fair comparison. Our contribution demonstrates that our approach enhances the model's robustness and performance using the existing user-specific data without additional data.
G.2. Evaluation Protocol Compared to the Original Dataset Paper
The original GRABMyo dataset paper did not directly evaluate gesture classification accuracy or provide corresponding benchmarks for classification tasks. Instead, the dataset's quality was assessed using metrics like the Area Under the Curve (AUC) and Equal Error Rate (EER). Therefore, our work does not follow the exact evaluation protocol proposed in the original paper because such protocol for gesture classification was not established.
G.3. Details of Evaluation Metrics
Root Mean Square Error (RMSE) is common metric for evaluating the deviation between predicted and observed values. As the range of fluctuations in the curves of different joint angles can vary significantly, is difficult to fairly evaluate the performance models using RMSE alone. Normalization of RMSE addresses this issue, resulting in the Normalized RMSE (NRMSE).
N
(Oeat Oreat)² 2
RMSE
N
(9)
RMSE
NRMSE
Omax
0min
Pearson Correlation Coefficient (CC) a widely used measure the linear relationship between two variables. ranges from -1 1, where larger CC value indicates greater similarity between the predicted and estimated joint angles curve,
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Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in sEMG Analysis
indicating improved estimation.
Ei=1 (Oess Oest) (Oreal Oreal
CC
(10)
E (fes Beal 2
(Greal
Oreal
===1
where Best and Dest are the estimated angle and their average, while Oreat and Oreat are the real angle and their average. 0max is the maximum of the real angle, and mmin is the minimum of the real angle.
H. Inference Performance and Parameter Comparison of Models
Model
Inference Time
Inference Time
Parameter Count
GPU Memory
GPU (A6000)
CPU (AMD EPYC 7543)
Allocated
Transformer
3.8 ms
15,1 ms
481169
18.08 MB
Add STEM with weight sharing Without weight sharing
3.9 ms
17.6 ms
489233
23.66 MB
4.8 ms
27.5 ms
581137
21.65 MB
Table 8: The comparison of inference time, number of parameters, and GPU usage between the model using STEM and a non-weight sharing transformer layer.
In Table 8, we use the following hyperparameters: Feature dimension is 12, the maximum length is 200, model dimension 64, number of attention heads is 2, number of layers is 3, dimension of feedforward network is 256, number of classes 17, the dropout rate is 0.1, positional encoding is 'learnable', the activation function is 'GELU', and normalization is 'BatchNorm'. The STEM model uses the same parameters as those in the experimental setup of the paper.
Parameter Count: By incorporating STEM, we significantly reduce the model's 's parameter count. Specifically, when the STEM module added (with weight sharing enabled), the parameter count increases slightly from 481,169 to 489,233, an increase of about 1.7%. However, if weight-sharing not used, the parameter count increases substantially to 581,137, a 21% increase.
Inference Time: We measured the inference time on both GPU and CPU. When using the STEM module with weight sharing, the GPU inference time is 3.9ms, and the CPU inference time is 17.6ms, which is significant improvement compared to case without the STEM module (4.8ms on GPU and 27.5ms on CPU). This indicates that while enhancing short-term features, the STEM module maintains low inference time.
GPU Memory Consumption: The relative increase in GPU usage is due to the mechanism of parallel computation that the GPU activates when using a sliding window.
H.1. Parameters search and the model's sensitivity
The selection of the masking ratio (0.15) and the average length of masked segments was informed by a systematic hyperparameter search using the (0.05, 0.15, 0.25, 0.35, 0.45), (1,3,5,799) range. This was conducted via Wandb, ensuring comprehensive evaluation of the model's sensitivity to these parameters. We observed that masking ratio of 0.15 and an average length of 3 consistently yielded the best performance in terms of classification accuracy. While the accuracy fluctuated by approximately 4% across the tested range, the 0.15 ratio provided an optimal balance between introducing sufficient noise for robust feature learning and retaining enough original signal for effective pretraining. The choice of segment length further complements this masking strategy, as it ensures that the model learns to reconstruct meaningful patterns while not overly relying on adjacent unmasked data. These parameters jointly enable the model focus on capturing intrinsic signal variability, thereby enhancing its resilience real-world noise scenarios. Such robustness aligns well with our goal of improving generalization in noisy environments.
Regarding window sizes of STEM, we experimented with varying configurations and found that short-term window size of 41 and 21 with a step size of provided the best balance between noise isolation and feature granularity. We use Wandb The accuracy fluctuated by approximately 3%
search for the best window size: the search space is (11,21,31,41,511, across the tested range. In the future, we will collaborate with biologists to explore the relationship between window size and different gesture classifications on biological level.
18


Reviews:
[{"review_id": "DuVp8v1joh", "reviewer": "Reviewer_AUAg", "summary": "The paper addresses the problem of noise resilience in surface electromyography (sEMG)-based gesture recognition. The authors propose a novel Short-Term Enhancement Module (STEM), which focuses on capturing short-term dependencies in sEMG signals to enhance noise resistance. Further, results on GRABMyo and Ninapro DB2 datasets show >20% improvement in noise resilience compared to existing models.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "# Response to Reviewer AUAg\n\nWe sincerely thank you for your thorough review and **insightful feedback** on our paper. We particularly appreciate your recognition of our **well-motivated problem statement** and the **novel approach of using short-term feature extraction**. Your professional suggestions have been invaluable in improving our work.\n\n## Regarding N-fold Cross-validation\n\nThank you for this excellent suggestion. While we initially followed standard evaluation methods in the field of sEMG-based gesture recognition (as seen in [1], [2], [3]) and used validation sets to prevent overfitting (detailed in the appendix's dataset section), we also believe that your suggestion is extremely valuable.\n\nAs per your recommendation, we have conducted a **6-fold cross-validation** comparing our method with the second-best performer (Informer):\n\n| Method | Average Accuracy | Standard Deviation |\n|--------|------------------|-------------------|\n| **STET (Ours)** | **92.15%** | **1.12** |\n| Informer | 88.06% | 1.56 |\n\nStatistical significance was confirmed with a t-test (p < 0.001), further supporting the robustness of our approach.\n\n## Concern about error bars\nWe apologize for any confusion. We would like to clarify that we have indeed included the mean and standard deviation in Table 1 of our original manuscript, as well as in our newly added n-fold experiments.\nWe appreciate your suggestion and recognize the importance of clearly presenting statistical significance when comparing our approach with the baselines. Based on your feedback, we will revise the manuscript to make these error metrics more prominent and ensure they are easily noticeable throughout our experimental results section.\n\n## Regarding Inference Latency\n\nWe completely agree with your insightful point about the importance of latency evaluation for downstream applications. While this information was included in our appendix under \"Inference Performance and Parameter Comparison of Models,\" we have expanded this section with additional comparative analysis:\n\n| Model | Inference Time by GPU (A6000) | Inference Time by CPU (AMD EPYC 7543) | Average Accuracy |\n|-------|------------------------------|--------------------------------------|-----------------|\n| Transformer (same layer without weight sharing) | 4.8 ms | 27.5 ms | 85.26% |\n| TCN | 2.1 ms | 10.6 ms | 81.50% |\n| GRU | 6.0 ms | 36.0 ms | 86.30% |\n| LST-EMG-NET | 5.2 ms | 31.0 ms | 85.31% |\n| **STEM (Ours)** | **3.9 ms** | **17.6 ms** | **90.76%** |\n\nOur STEM module adds minimal computational overhead (only 0.1ms on GPU and 2ms on CPU) while delivering **superior accuracy with competitive inference speed**.\n\n## Conclusion\n\nOnce again, we are grateful for your expert review and constructive feedback. We hope the additional evaluations address the concerns you raised while further validating the effectiveness of our approach. Your positive assessment is encouraging to our research team.\n\n[1] Multi-attention feature fusion network for accurate estimation of finger kinematics from surface electromyographic signals IEEE Transactions on Human-Machine Systems\n\n[2] Cross-Subject Lifelong Learning for Continuous Estimation from Surface Electromyographic Signal IEEE Transactions on Neural Systems and Rehabilitation Engineering\n\n[3] A CNN-attention network for continuous estimation of finger kinematics from surface electromyography IEEE Robotics and Automation Letters", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "Yes.", "theoretical_claims": "It is an empirical paper.", "experimental_designs_or_analyses": "The experiments are missing a robust evaluation based on either Leave one user out or n-fold cross validation to show there is no overfitting in the results.", "relation_to_broader_scientific_literature": "The paper builds on previous works and advances sEMG-based gesture recognition by focusing on short-term dependencies.", "essential_references_not_discussed": "Not to my knowledge.", "other_strengths_and_weaknesses": "Strengths:\n- The paper is clean and organized.\n- Well motivated problem and novel idea of using short-term feature extraction.\n\nWeaknesses:\n- Missing robust evaluation of the existing dataset using Leave-one-subject-out (LOSO) evaluation or n-fold cross-validation.\n- Since, the gains are small compared to the baselines. It would be good to know the error bar (mean, stddev) of the results and baselines.\n- Any evaluation of latency of the system is missing compared to previous works.", "other_comments_or_suggestions": "No", "questions_for_authors": "Please refer to strengths and weaknesses section for the rebuttal.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ihznnKgnQV", "reviewer": "Reviewer_ybGK", "summary": "This paper specially captures the short-term temporal dependencies in sEMG-based gesture recognition. By designed a self-supervised pretrained method and two short/long-term heads, the proposed method achieve SOTA performance.\n\n## update after rebuttal\nThe authors have resolved most of my concerns.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "# Response to Reviewer ybGK\n\nWe sincerely appreciate your thorough review and valuable feedback on our manuscript. We are  grateful for your recognition of our paper's strengths, including the **clear writing style**, **effective visualizations**, **strong performance** , **comprehensive ablation studies**, and the **novelty of our approach**. Your positive assessment is greatly encouraging to our research team.\n\nWe have carefully considered your concerns and would like to address them as follows:\n\n## Regarding Dataset Evaluation\n\nWe apologize for causing this misunderstanding. While we did indeed evaluate our method on **both GRABMyo (results presented in Table 1) and Ninapro DB2 (results presented in Table 4)**, we acknowledge that this was not sufficiently clear in our presentation. We will revise the manuscript to clearly indicate the comprehensive nature of our evaluation.\n\nFurthermore, to provide even more robust evaluation, we have **added results on an additional dataset, Ninapro DB5**, as shown in the table below:\n\n| Model       | Accuracy (%) |\n|-------------|--------------|\n| LST-EMG-Net | 82.23        |\n| Informer    | 85.22        |\n| TEMGNET     | 80.74        |\n| STET (ours) | **87.61**    |\n\nThis enhancement brings our total to **three distinct datasets**, offering a more comprehensive assessment of our method's performance across varied conditions.\n\n## Regarding Comparison Methods\n\nWe thank you for highlighting the need for more recent comparison methods. Following your suggestion, we have **expanded our comparisons to include two state-of-the-art methods published in late 2024**:\n\n| Model                   | GRABMyo (ACC) | DB2 (PCC) | DB5 (ACC) |\n|-------------------------|---------------|-----------|-----------|\n| Spgesture (NeurIPS 2024)| 88.06%        | 0.84      | 86.32     |\n| LRNN (TIST 2024)        | 86.75%        | 0.82      | 86.20     |\n| STET (ours)             | **90.76%**    | **0.88**  | **87.61** |\n\nThese additional comparisons demonstrate that **our method maintains its performance advantage even against the most recent approaches** in the field.\n\n## Our Main Contributions\n\nTo summarize, we would like to reiterate the key contributions of our work:\n\n1. **Introduction of sEMG Signal Masking**: We propose a novel self-supervised pretraining technique using sEMG Signal Masking (Sensor-wise and Contiguous Masked Segments following a Geometric Distribution) to leverage the inherent variability in sEMG data.\n\n2. **STEM Module for Enhanced Noise Resilience**: From the perspective of improving short-term feature representation, we propose STEM, an adaptive and noise-resistant module. Integrating STEM into various neural networks has demonstrated significant performance gains.\n\n3. **Improved Noise Resilience**: Extensive experiments confirm that our overall design substantially enhances the noise resilience of models for sEMG data.\n\nWe sincerely appreciate your insightful comments, which have helped us improve the quality and comprehensiveness of our manuscript.", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "Yes", "theoretical_claims": "Yes", "experimental_designs_or_analyses": "Yes", "relation_to_broader_scientific_literature": "N/A", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "Pros:\n1. This papre is well written and easy to understand with clear visualizations.\n2. The proposed methods achieves strong performance.\n3. The authors give plentiful ablations and visualizations to support the method.\n4. The proposed method seems novel with a new MAE-based self-supervised pre-training method and two heads to capture short/long-term temporal dependencies.\n\nCons:\n1. The methods included for comparison are old. Most are published before 2021 and only one of them is published in 2023. It seems that this area has drawn little attention in recent years, or authors have neglected several recent works.\n2. The authors claim that they use two datasets for evaluation (GRABMyo and the Ninapro DB2, line 113), but i only observe results on one dataset (tab.1). It seems that the results are insufficient.\n3. While the proposed method seems novel, some of the components have been used in previous methods. For example, MAE-based self-supervised pre-training is widely used in previous self-supervised methods. The long-term modeling method is a simple attention implementations, while the short-term modeling method is implemented by windowed attention.", "other_comments_or_suggestions": "N/A", "questions_for_authors": "My overall concerns focus on the old methods included for comparison, and whether the authors use adequate datasets for evaluation.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "sfmerbaxwn", "reviewer": "Reviewer_QgDP", "summary": "This paper proposes a noise-robust method for surface electromyography (sEMG)-based gesture recognition. The authors emphasize the importance of short-term signal learning in mitigating the interference of local noise, which could otherwise degrade the modeling of long-term signals. Specifically, the paper introduces an sEMG module that separately models long-term and short-term signal features. Within this module, the authors propose a advanced masking strategy to prevent excessive signal isolation, a common issue when applying standard masking techniques in masked autoencoders. Additionally, they propose an Asymmetric Optimization method that prioritizes difficult cases during training, enhancing model robustness.The effectiveness of these designs is validated through extensive experiments, demonstrating significant improvements in noise resistance and overall gesture recognition performance.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "# Response to Reviewer QgDP\n\nThank you for your professional review and valuable time. Your positive assessment is incredibly encouraging to our research team. We sincerely appreciate your thoughtful comments and would like to address each of your concerns as follows:\n\n## Broader Scientific Literature\n\nThank you for bringing this to our attention. We will discuss these two critical works (SCN, SpGesture) in the camera-ready version from the perspective of SNN development in sEMG gesture recognition.\n\n## Focal Loss vs Asymmetric Optimization\n\nThank you very much for bringing this to our attention. Although this was not our core contribution, we appreciate that providing these results would offer readers more valuable insights.\n\nWe have conducted additional tests on the Grabmyo:\n- STET + Focal Loss: 89.42% \n- STET + Asymmetric Optimization: 90.54% \n\n## Modeling in Non-Parallel vs Parallel\n\nThank you for your suggestion. We compared the Focal Transformer (non-parallel) with our method. \n\n| Backbone | Grabmyo (ACC) | GPU-A6000-Latency (ms) |\n|----------|---------------|------------------------|\n| Focal Transformer | 84.56 | 4.7 |\n| STET | 90.72 | 3.9 |\n\nIn our inference speed experiments, we used a 4-stage Focal Transformer, while STET used two long-term layers plus two short-term layers. \n\n## Concerns about Hard Samples\n\nWe appreciate your insightful question. Below is our detailed response:\n### Definition of Hard Samples\n**Hard Positives**:\n   - **Definition**: Positive samples (ground-truth $y_{i,j} = 1$) with low predicted probabilities $\\widehat{y}_{i,j}$\n   - **Weighting Mechanism**: The term $(1 - \\widehat{y}_{i,j} )^{\\gamma^+}$ assigns higher weights to samples where the model lacks confidence.\n\n### Proportions and Dynamics\n\n| Sample Type | Estimated Hard Proportion | Remarks |\n|-------------|---------------------------|---------|\n| **Hard Positives** | 40%-60% (early training) | Decreases to ~20% as the model converges due to asymmetric focusing. |\n| **Hard Negatives** | 5%-10% (persistent) | Suppressed via $\\gamma^- < \\gamma^+$ and $m=0.2$; absolute count remains high but influence is reduced. |\n\n## Classic Mask Method vs Our Method\n\nThank you for your suggestion! We actually included related experiments in Appendix A.3. Classical mask uses a standard pretraining with a general mask, employing a standard masking approach.\n\n| Backbone | Masking method | AG Noise↓ (%) | MG Noise↓ (%) |\n|----------|----------------|--------------|--------------|\n| Transformer | classical | 22.00 | 15.00 |\n| Transformer | ours | 15.00 | 13.00 |\n\nWe understand your concern and will highlight this experiment more prominently in the revised paper.\n\n\n## Concerns about  Noise Reduction\n\nWe appreciate your thoughtful comment. We would like to clarify this relationship with additional insights and reasoning.\n\nThe connection between short-term feature modeling and noise resilience is rooted in the fundamental characteristics of sEMG signals and common noise patterns. \n1. **Electrode-skin interface noise**: Movement artifacts and momentary changes in skin-electrode contact typically manifest as brief bursts of interference rather than consistent long-term corruption [1, 2].\n2. **Environmental electromagnetic interference**: Such interference usually occurs in short, sporadic patterns rather than continuously affecting the entire signal [3].\n\nIt's worth noting that we're **not focusing on constant background noise** (such as power line interference at 50/60Hz), as these can be effectively removed through conventional filtering techniques. Our primary concern is with **transient, unpredictable noise patterns** that are more challenging to address through traditional methods and represent common challenges in real-world applications.\n\nBy adopting a sliding window attention mechanism, our STEM concentrates on local temporal windows to isolate noise within affected segments, preventing it from spreading across the entire sequence. This method outperforms the long-term decoder under various noise types (as shown in Figure 3) and offers multiple sampling perspectives: overlapping windows that partially include noisy regions can still capture valuable information, thus reinforcing resilience.\n\n**We believe visualizing the noise distribution is a worthwhile idea**; however, precisely pinpointing the noise remains challenging, so we cannot present such a visualization yet, **but we will continue to work toward it**.\n\n\n### References\n[1] Surface electromyography: physiology, engineering, and applications. IEEE Press.\n\n[2] Filtering the surface EMG signal: Movement artifact and baseline noise contamination. Journal of Biomechanics\n\n[3] Sampling, noise-reduction and amplitude estimation issues in surface electromyography. Journal of Electromyography and Kinesiology\n\nWe sincerely appreciate all your valuable suggestions and will incorporate them into our revised manuscript. Your positive assessment is greatly encouraging to our research team.", "claims_and_evidence": "The validation experiments in Fig. 3 demonstrate the effectiveness of the proposed methods. However, I still have some doubts about whether the reduction of noise interference is directly attributed to the enhancement of short-term feature modeling. While it is a common sense that combining short- and long-term feature modeling generally improves performance, the specific contribution of short-term modeling in mitigating noise interference remains unclear. o strengthen this claim, additional qualitative analysis is needed. For instance, providing insights into the proportion of data affected by local noise and the duration distribution of such noise would help clarify the impact of short-term modeling on noise robustness.", "methods_and_evaluation_criteria": "The dataset and evaluation metrics are reasonable in this paper.", "theoretical_claims": "I have reviewed the algorithm for sEMG signal masking and the arguments regarding the advantages of short- and long-term signal modeling. However, the theoretical analysis supporting how short-term modeling improves the model's robustness to noise is currently lacking.", "experimental_designs_or_analyses": "I have reviewed the experimental design, particularly the noise robustness evaluation in Fig. 3. While the results suggest improved performance under different noise conditions, the paper does not provide details on how the noise levels were set or whether they align with real-world conditions. A more rigorous analysis, such as comparing against real sEMG noise distributions, would strengthen the validity of the conclusions.", "relation_to_broader_scientific_literature": "[1] A novel event-driven spiking convolutional neural network for electromyography pattern recognition. [2] SpGesture: Source-Free Domain-adaptive sEMG-based Gesture Recognition with Jaccard Attentive Spiking Neural Network haven't been discussed and compared in this paper.", "essential_references_not_discussed": "I haven't found related papers that were not discussed in this paper.", "other_strengths_and_weaknesses": "1. The paper lacks a direct comparison between employing Focal Loss and Asymmetric Optimization. Evaluating their respective impacts on model performance would provide a clearer justification for the proposed optimization strategy.\n2. There is no comparison between conducting long-term and short-term modeling in a non-parallel mode. Additionally, the computational cost differences between the parallel and non-parallel approaches should be supported by experimental results, not just a claim.\n3. What types of examples are identified as difficult, and why? Furthermore, what is their proportion in the dataset? Providing more details on this aspect would help clarify the role of Asymmetric Optimization in handling hard cases.\n4. Have you considered directly applying the typical masked strategy used in standard reconstruction methods? It would be helpful to provide experimental results comparing this approach with your proposed strategy.\n5. What is the proportion of difficult cases in the dataset? Additionally, how does model performance vary across different difficulty levels?", "other_comments_or_suggestions": "No.", "questions_for_authors": "No.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This work deals with the problem of noise resilience in surface electromyography-based gesture recognition. The authors introduced a new Short-Term Enhancement Module, that focuses on capturing short-term dependencies in sEMG signals to enhance noise resistance. Overall, the method is quite interesting and the idea of using short-term feature extraction is quite novel. All the reviewers recommend acceptance. AC agrees with the reviewers.
Decision: Accept (poster)