WASSL-EGR: Wavelet Attention and State-Space Learning for Robust EMG-Based Gesture Recognition

Hybrid feature fusion with deep learning framework for sEMG based airwriting recognition

Classification of Gesture Electromyography by Dynamic Mode Decomposition

Accurate classification of hand gestures from surface electromyography (sEMG) signals is essential for human-computer interaction and myoelectric prosthetic control, yet conventional feature-extraction methods struggle to capture the temporal dynamics of muscle activation. Dynamic Mode Decomposition (DMD), a data-driven technique originally developed for fluid dynamics, has not been extensively validated for sEMG signals. This study addresses that gap by introducing DMD as a feature-extraction framework for sEMG-based gesture classification, providing a mathematically rigorous approach for capturing spatiotemporal muscle-activation patterns. Using sEMG recordings from 37 participants, we extracted DMD-reconstructed features–Mean Absolute Value (MAV), Root Mean Square (RMS), Simple Square Integral (SSI), Variance (VAR), Standard Deviation (STD), Median, Integrated EMG (IEMG), and spectral features–and compared their discriminative power against traditional features across multiple classifiers. DMD-derived features significantly distinguished common hand gestures ( $p \lt 0.05$ , Bonferroni-corrected) and consistently outperformed traditional features: MAV, RMS, SSI, and VAR achieved 96% accuracy with k-nearest neighbors and 88% with random forest, while support vector machines reached 97% and 96% accuracy using STD, MAV, and RMS, respectively. Convolutional Neural Networks and Deep Stacked Neural Networks achieved up to 85% and 87% accuracy, respectively, using DMD-extracted spectral features. These results establish DMD as an effective, interpretable feature-extraction method for sEMG-based gesture classification, with applications in rehabilitation engineering, prosthetic control, and human-computer interaction.

LT-RNet: A Hybrid BILSTM-Transformer Network for sEMG Hand Gesture Recognition

A Multimodal Gesture Recognition Framework Using sEMG and ACC Signals with Cross-Temporal Attention and Mutual Information Regularization

Surface electromyography (sEMG), which reflects temporal muscle activity, has been widely applied in gesture recognition and human-computer interaction. However, single-modal sEMG signals are susceptible to factors such as electrode displacement, inter-subject variability, and noise, which limit recognition performance. To improve system robustness, this paper proposes a multimodal gesture recognition method based on sEMG and acceleration (ACC) signals. First, temporal convolutional encoders are employed to extract temporal features from the sEMG and ACC signals, respectively. Then, a Cross-Temporal Attention mechanism is introduced to fuse features from different modalities, thereby modeling the temporal correlations between multimodal signals. In addition, Mutual Information Neural Estimation (MINE) is adopted to constrain the mutual information between features of different modalities, so as to enhance the consistency and discriminative capability of the fused features. To verify the effectiveness of the proposed method, a multimodal gesture dataset containing 11 subjects, 10 gestures, and 4 different postures was constructed. Experiments were conducted using five-fold cross-validation within each subject. Experimental results show that, compared with single-modal methods and simple fusion methods, the proposed method achieves superior performance in both recognition accuracy and Macro-F1 score, with an average recognition accuracy of 92.41%. These results demonstrate that the proposed method can effectively improve multimodal gesture recognition performance and has strong application potential.

Impact of Sampling Rate and Quantization on EMG-Based HMI: Achieving Hardware Invariance by Advanced Feature Engineering

Electromyography (EMG) serves as a robust signal source for developing intuitive human-machine interfaces (HMIs). With the proliferation of commercial and prototype EMG armbands, there is a growing need to balance high-accuracy gesture recognition with hardware constraints. While deep learning architectures have achieved remarkable performance, they often require significant computational resources and high-end sensors. This study investigates an algorithmic-centric perspective, exploring whether sophisticated feature extraction (FE) can compensate for reduced hardware specifications. Using a 20-gesture dataset collected from 10 subjects, we evaluated performance across three hardware configurations: 500 Hz/8-bit (C1), 500 Hz/12-bit (C2), and 1000 Hz/8-bit (C3). We compared traditional FE methods against modern paradigms designed to capture spatial and spatio-temporal signal dynamics, specifically PHASOR, Myoelectric Temporal Patching (MTP), and the proposed WaveLSTM (Wave long short-term memory) framework. WaveLSTM embeds deep-learning-inspired processing schemes, i.e., utilizing short- and long-term memory components, into a computationally efficient pipeline with significantly lower overhead than standard deep learning models. Our results demonstrate that WaveLSTM achieves superior accuracy, exceeding 95% in a leave-one-trial-out validation scheme. Notably, while classical FE approaches exhibited lower overall recognition rates, WaveLSTM demonstrated hardware invariance, maintaining high performance regardless of changes in sampling frequency (p=0.275) or ADC resolution (p=0.941). These findings suggest that advanced spatio-temporal feature engineering can enable high-performance, complex gesture recognition on low-cost, low-power wearable devices without sacrificing accuracy.

NewRep: a classification model for online pattern recognition based on HD-EMG signals.