Real time motion intention recognition method with limited number of surface electromyography sensors for A 7-DOF hand/wrist rehabilitation exoskeleton

The functions of human hand are rich, and the motor dysfunction of hand of chronic stroke patients can be alleviated to some extent through active rehabilitation training. Hand rehabilitation exoskeleton can assist pa-tients to do active rehabilitation training. However, how to realize more motion with less surface electro-myogrphy (sEMG) sensors, and how to realize the real-time motion intention recognition are two important issues. This paper introduces real-time motion intention recognition method with limited number of sEMG sensors for a 7-DOF wearable hand/wrist rehabilitation exoskeleton to realize the real-time motion intention recognition and rehabilitation training. Root mean square (RMS) and Bens Spiker Algorithm (BSA) features of three-channel sEMG signals are extracted, and they are mapped to seven different intention movements by combining the Bagging method. The finger structure part of the exoskeleton is composed of a rotary-spatial -spatial-rotary (RSSR) mechanism and a double-parallelogram mechanism, which makes the projection center of exoskeleton coincide with the rotation center of the hand joint. The average real-time motion intention recognition accuracy is 95.37 +/- 0.97%.

Automated summary

The wearable hand/wrist rehabilitation exoskeleton with limited number of sEMG sensors achieved real-time motion intention recognition, resulting in improved rehabilitation training for chronic stroke patients.