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DSCAttenEMG: A Lightweight sEMG-Based Hand Gesture Recognition Model via Depthwise Separable Convolution and Multi-Head Attention
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DOI：10.1109/tmc.2026.3697898.png)
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Abstract
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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></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.
Keywords:
Mobile computing
sEMG-based hand gesture recognition
lightweight neural network
depthwise separable convolution
multi-head self-attention
embedded deployment
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