Upper Limb Movement Prediction Based on Segmented sEMG Signals

Motion intent recognition research is one of the key challenges in achieving human-robot collaboration in rehabilitation robots. In the traditional method, intention recognition is performed based on the complete sEMG, however, due to the muscle atrophy of stroke patients, the complete sequences are not captured at the early stage of rehabilitation, so in this paper, three sEMG segments of 1/2, 1/4, and 1/8 of three selected activity of daily living (ADL) movements of the upper limb are investigated, respectively, and comparing with the complete sEMG sequences, a novel method of motor intention prediction is proposed. In order to achieve optimal recognition accuracy and speed, the Kernel Extreme Learning Machine (KELM) algorithm optimized by the Sparrow Search Algorithm (SSA) algorithm was used for prediction. It was found that the SSA-KELM algorithm based on segmented sEMG has better recognition accuracy and recognition speed in each segment compared to other algorithms. The recognition accuracy in 1/8 sEMG segments is 98.4%, and the recognition time is 0.0102s, which shows how well the method works and what it means for rehabilitation robots working together with people.