An efficient surface electromyography-based gesture recognition algorithm based on multiscale fusion convolution and channel attention

In the field of rehabilitation, although deep learning have been widely used in multitype gesture recognition via surface electromyography (sEMG), their higher algorithmic complexity often leads to low computationally inefficient, which compromise their practicality. To achieve more efficient multitype recognition, We propose the Residual-Inception-Efficient (RIE) model, which integrates Inception and efficient channel attention (ECA). The Inception, which is a multiscale fusion convolutional module, is adopted to enhance the ability to extract sEMG features. It uses fast dimensionality reduction, asymmetric convolution decomposition, and pooling to suppress the accumulation of parameters, then reducing the algorithmic complexity; The ECA is adopted to reweight the output features of Inception in different channels, enabling the RIE model to focus on information that is more relevant to gestures. 52-, 49-, and 52-class gesture recognition experiments are conducted on NinaPro DB1, DB3, and DB4 datasets, which contain 14,040, 3234, and 3120 gesture samples, respectively. RIE model proposed in this study achieves accuracies of 88.27%, 69.52%, and 84.55% on the three datasets, exhibiting excellent recognition accuracy and strong generalization ability. Moreover, this method reduces the algorithmic complexity from both spatial and temporal aspects, rendering it smaller in size and faster in computation compared to other lightweight algorithms. Therefore, the proposed RIE model possesses both lightweight computational requirements and reliable performance, providing an efficient deep learning method for gesture recognition based on sEMG.