An Exploratory Study of Single Channel Surface Electromyography for Hand Gesture Classification

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.

A Comparative Study of sEMG Gesture Recognition Algorithms Based on Ninapro DB1

: Due to the accelerating aging of the population and the continuous increase in the number of stroke patients, there is an urgent need for rehabilitation robotics technology with high-precision gesture recognition capabilities. Surface electromyography (sEMG) signals, as an important type of bioelectric signal, can reflect human movement intentions. However, their non-stationary nature and low signal-to-noise ratio pose challenges for recognition algorithms. This study utilized the publicly available Ninapro DB1 dataset and employed a standardized preprocessing and feature extraction workflow to systematically compare the performance of four machine learning algorithms — Random Forest (RF), Multi-Layer Perceptron (MLP), LightGBM, and K-Nearest Neighbors (KNN) — in sEMG gesture recognition tasks. Experiments were conducted using 10-fold cross-validation, with performance evaluated across multiple metrics including accuracy, precision, recall, and F1 score. The results show that LightGBM performs best across all metrics (all exceeding 88.5%), demonstrating strong feature learning and generalization capabilities. This study provides empirical evidence for the selection of sEMG gesture recognition algorithms and offers guidance for the practical application of rehabilitation robot systems.

Sensor Topology-Aware Three-Branch Fusion for sEMG Gesture Recognition

A three-branch fusion network that explicitly models the ring arrangement of armband electrodes, capturing the adjacency information in the sensor topology that linear channel representations ignore and generalizes to MyoArmbandDataset under a subject-adaptive transfer learning protocol without dataset-specific hyperparameter retuning.

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

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

Surface electromyography (sEMG) signals enable intuitive human-machine interaction by capturing muscle activation patterns associated with hand gestures. However, the accurate recognition of hand gestures using sEMG signals still remains challenging due to the complex nature of the signal variations. This work proposes a wavelet-based deep learning framework for EMG gesture recognition using time-frequency representations. The segmented sEMG signal is converted into the Continuous Wavelet Transform (CWT) spectrogram to obtain the multi-scale muscle activation. Two lightweight deep learning frameworks are developed to effectively learn gesture representations from these wavelet features. The Wavelet Attention Convolutional Neural Network (WA-CNN) integrates scale and channel attention to emphasize informative frequency bands and EMG electrodes. The Wavelet State-Space Model (WSSM) incorporates efficient temporal modeling of gesture dynamics. Experimental results on the Ninapro DB2 dataset demonstrate that the proposed models outperform conventional machine learning and deep learning baselines. In particular, WSSM achieves 95% accuracy while maintaining low model complexity suitable for real-time wearable EMG-based interaction systems.

Machine Learning-Based Hand Gesture Recognition Using Wearable Sensor Data

This paper presents a novel approach to hand gesture recognition based on machine learning techniques applied to data collected from wearable sensors. The proposed system aims to accurately identify intuitive hand gestures to facilitate the development of contactless user interfaces. To achieve robust and reliable recognition performance, various feature extraction methods were systematically employed, and an ensemble learning strategy was developed using Support Vector Machines (SVM), Random Forests (RF), Gradient Boosting (GB), and a Voting Classifier (VC). The gesture dataset utilized in this study consists of over 4,000 samples collected from multiple individuals, capturing diverse gesture classes and conditions. Rigorous experimental evaluation demonstrates that the proposed ensemble approach attains superior accuracy and robustness compared to traditional single-model methods. This research contributes valuable methodological insights and provides an accessible, scalable framework that enhances the practical applicability of sensor-based gesture recognition systems in diverse real-world applications.