Knowledge distillation for sEMG-based gesture recognition: Enhancing wearable HMI systems with lightweight models

OBJECTIVE: Gesture recognition using surface electromyography (sEMG) is a promising technology for wearable human-machine interaction (HMI) 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 CNN and 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 (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.