A Framework for Human-Exoskeleton Interaction Based on sEMG Interface and Electrotactile Feedback

The lower extremity rehabilitation exoskeleton provides a priceless opportunity for paraplegics to stand up and walk normally. In this paper, a framework for human-exoskeleton interaction with sEMG interface and electrotactile feedback is presented. Firstly, the exoskeleton recognises human motion intention through the Long Short Term Memory (LSTM) Neural Network based on surface electromyogram (sEMG). Secondly, electrotactile based on spatiotemporal coding scheme feeds back the exoskeleton states to the sensory skin, as a compensation for paraplegic's absent proprioception in lower limbs. Thirdly, physiological information, i.e. muscle fatigue, of arms is monitored and quantified with sEMG. Three healthy subjects participate the experiments including the motion intention recognition experiment, electrotactile perception experiment and online exoskeleton experiment. The online exoskeleton experiments demonstrate the feasibility and efficiency of the proposed framework. Furthermore, it is proved experimentally that the performance of collaborative control in rehabilitation human-exoskeleton interaction can be facilitated via the framework.