A Stratified sEMG Feature Extraction Method for Hand Gesture Recognition

Surface electromyographic (sEMG) signals contain rich motion information and have a wide range of applications in prosthetics, medical rehabilitation, and muscle status assessment. Feature extraction of sEMG signals is a commonly used and effective approach in the analysis and application of sEMG. This article proposes a novel stratified feature extraction method aimed at capturing the feature information of sEMG signals under different time-domain (TD) intensity levels. The method divides the sEMG signal into different amplitude ranges to obtain the muscle activity frequency information under different intensity levels. First, the threshold value of each layer of each channel is updated in real time to obtain the sEMG signals under the corresponding TD intensity. Second, the number of zero crossing (ZC) features that satisfy the intensity requirements is extracted and combined sequentially with the number of ZC features extracted in the unstratified case to form a feature dataset. Finally, the sEMG signals are divided into several layers to explore the impact of each layer on accuracy. Experiments on a self-built dataset and the publicly available DB5 dataset reveal that accuracy rises initially and then declines gradually with increasing layers. On the self-built dataset, the highest accuracy of 98.22% is achieved at the fourth layer compared to the unstratified extraction with an improvement of 8.74%. In the E2 and E3 of the DB5 dataset, the highest accuracies of 92.16% and 91.37% are achieved, respectively, at the third layer. This indicates that the proposed innovative stratified feature extraction method can significantly improve the accuracy of gesture recognition and effectively expand the applicability of sEMG signal-based action recognition methods.