Sep 2026· IEEE Transactions on Mobile Computing· Vol 25, pp. 15280-15292· 0 citations· 76 references
Abstract
The implementation of surface electromyography (sEMG)-based hand gesture recognition on mobile and wearable systems is frequently restricted by the finite computing, memory, and battery capabilities of edge devices. Even though a low-density sEMG setup is a feasible hardware implementation, achieving robust recognition under such constraint conditions becomes very challenging due to the non-stationary nature and inter-subject variance. In this paper, we propose DSCAttenEMG, an efficient neural network that combines both Depthwise Separable Convolution (DSC) for local feature extraction and Multi-Head Self-Attention (MHSA) to model long-range dependencies on EMG/IMU data, using 1× 1 DSC followed by Global Average Pooling to replace high-dimensional fully connected layers. Extensive experimentation on a self-collected dataset, the public SeNic and BandMyo datasets shows that our approach achieves state-of-the-art recognition performance (94.45%, 94.11% and 92.89%) at negligible complexity (only 178–179 K parameters). The model is capable of real-time inference (0.93 ms on RTX 4090 GPU, 6.68 ms on NVIDIA Jetson AGX Orin, 1.4/0.7 ms on CPU/NPU of Qualcomm mobile platform) and has a high degree of practicality for embedded deployment (118 samples/s at <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math><alternatives><mml:math><mml:mo>∼</mml:mo></mml:math><inline-graphic xlink:href="wen-ieq1-3697898.gif"/></alternatives></inline-formula>1 W on K230 edge AI platform). This amalgamation of three pivotal strengths, elevated accuracy, enhanced efficiency, and pragmatic viability, highlights its substantial potential for practical mobile and wearable applications.
Objective. Gesture recognition using surface electromyography (sEMG) is a promising technology for wearable human–machine interaction systems. However, sEMG-based gesture classification models often suffer from performance degradation in cross-user scenarios due to the variability in individual physiological signals. To address this issue, large-scale deep learning algorithms with a great amount of parameters are employed to enhance model generalization and improve recognition performance across different users. However, the use of deep learning models on wearable devices is challenging due to their limited computational power and memory capacity. Methods. This study addresses this challenge through knowledge distillation, which compresses large teacher models into lightweight student models while maintaining performance. We trained deep teacher models, including DenseNet, InceptionV3, and VggNet, and distilled their parameters into compact convolutional neural network and long short-term memory (LSTM) models. The experiment was performed on the sEMG data of 30 subjects collected from a wrist-band electrode with 32 channels. Main Results. The results demonstrated that student models, particularly LSTM-based ones, achieved classification accuracy close to or even higher than their teacher models, with a highest accuracy improvement. Among the teacher–student combinations, the DenseNet121-LSTM architecture achieved the highest classification accuracy. The relationship between computational complexity floating-point operations (FLOPs) and model performance was also analyzed, showing that the distilled models can effectively approximate high-FLOP models. Significance. This research enhances the feasibility of embedding deep learning models into wearable systems (e.g. wristband or wristwatch), facilitating more responsive and efficient gesture recognition in daily-life applications. The Python implementation of the complete knowledge distillation framework is publicly available at: https://github.com/Open-EXG/handDistill.
Fang Qiu, Chenyun Dai, Xiaodong Liu et al.· Journal of Neural Engineerin...· 0 citations
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.
Luoqi Cui, Yong Liu, Hadi Fathollahi Abdar et al.· Italian National Conference...· 0 citations
This study investigates the feasibility of classifying ten hand gestures using a single sEMG channel combined with lightweight machine learning architectures and demonstrates that combining time and frequency features with Pearson filtering and a compact NN can achieve up to 90 percent accuracy.
EMG-CrossFormer is introduced, an end-to-end hybrid convolutional-transformer for seamless multimodal integration that improves sEMG-only decoding and that multimodal fusion substantially amplifies this benefit, underscoring the value of both design principles for complex hand gesture recognition.
Federico Del Pup, E. Tentori, M. Atzori· 0 citations
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.
Rami N. Khushaba, F. Verdini, A. Mengarelli et al.· 2026 IEEE/ASME International...· 0 citations
: 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.
Weiliang Chen· Proceedings of the 3rd Inter...· 0 citations
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