Abstract

Surface electromyography (sEMG) signals are electrical signals released by muscles during movement, which can directly reflect the muscle conditions during various actions. When a series of continuous static actions are connected along the temporal axis, a sequential action is formed, which is more aligned with people's intuitive understanding of real-life movements. The signals acquired during sequential actions are known as sequential sEMG signals, including an additional dimension of sequence, embodying richer features compared to static sEMG signals. However, existing methods show inadequate utilization of the signals' sequential characteristics. Addressing these gaps, this paper introduces the Spatio-Temporal Feature Extraction Network (STFEN), which includes a Sequential Feature Analysis Module based on static-sequential knowledge transfer, and a Spatial Feature Analysis Module based on dynamic graph networks to analyze the internal relationships between the leads. The effectiveness of STFEN is tested on both modified publicly available datasets and on our acquired Arabic Digit Sequential Electromyography (ADSE) dataset. The results show that STFEN outperforms existing models in recognizing sequential sEMG signals. Experiments have confirmed the reliability and wide applicability of STFEN in analyzing complex muscle activities. Furthermore, this work also suggests STFEN's potential benefits in rehabilitation medicine, particularly for stroke recovery, and shows promising future applications.