DSC-GRUNet: A lightweight neural network model for multimodal gesture recognition based on depthwise separable convolutions and GRU

Abstract

With the advancement of human-computer interaction (HCI) technology, gesture recognition methods based on electromyography (EMG) signals have garnered widespread attention, particularly in fields such as rehabilitation medicine and smart prosthetics. However, traditional EMG-based gesture recognition methods face challenges, including insufficient accuracy and poor noise resistance when handling complex gestures and diverse scenarios. To address these challenges, this study proposes a lightweight gesture recognition network based on multimodal signal fusion, combining surface EMG and Acceleration (ACC) signals. The proposed model integrates Depthwise Separable Convolutions (DSC) and Gated Recursive Units (GRU) to achieve a lightweight design while maintaining recognition performance. Experimental results demonstrate that the proposed method achieves recognition accuracies of 92.03±3.28 % and 77.48±4.38 % on the NinaPro DB2 and DB5 datasets, respectively, outperforming other state-of-the-art methods in terms of efficiency and computational cost. Additionally, the fusion of multimodal data significantly enhances the recognition performance of dynamic gestures. This study provides new insights into the design of embedded, real-time gesture recognition systems and holds important practical implications.