WaveFormer: A Lightweight Transformer Model for sEMG-based Gesture Recognition

• URL: http://arxiv.org/abs/2506.11168v1
• Date: Thu, 12 Jun 2025 04:07:11 GMT
• Title: WaveFormer: A Lightweight Transformer Model for sEMG-based Gesture Recognition
• Authors: Yanlong Chen, Mattia Orlandi, Pierangelo Maria Rapa, Simone Benatti, Luca Benini, Yawei Li,
• Abstract summary: WaveFormer is a lightweight transformer-based architecture tailored for sEMG gesture recognition.<n>Our model integrates time-domain and frequency-domain features through a novel learnable wavelet transform, enhancing feature extraction.<n>With just 3.1 million parameters, WaveFormer achieves 95% classification accuracy on the EPN612 dataset, outperforming larger models.
• Score: 18.978031999678507
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human-machine interaction, particularly in prosthetic and robotic control, has seen progress with gesture recognition via surface electromyographic (sEMG) signals.However, classifying similar gestures that produce nearly identical muscle signals remains a challenge, often reducing classification accuracy. Traditional deep learning models for sEMG gesture recognition are large and computationally expensive, limiting their deployment on resource-constrained embedded systems. In this work, we propose WaveFormer, a lightweight transformer-based architecture tailored for sEMG gesture recognition. Our model integrates time-domain and frequency-domain features through a novel learnable wavelet transform, enhancing feature extraction. In particular, the WaveletConv module, a multi-level wavelet decomposition layer with depthwise separable convolution, ensures both efficiency and compactness. With just 3.1 million parameters, WaveFormer achieves 95% classification accuracy on the EPN612 dataset, outperforming larger models. Furthermore, when profiled on a laptop equipped with an Intel CPU, INT8 quantization achieves real-time deployment with a 6.75 ms inference latency.

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