The Human–Machine Interface Design Based on sEMG and Motor Imagery EEG for Lower Limb Exoskeleton Assistance System
Wenju Li, Yue Ma, Keyong Shao, Zhengkun Yi, Wujing Cao, Meng Yin, Tiantian Xu, Xinyu Wu
Abstract
In the lower limb exoskeleton assistance system, motion intention understanding based on biological signals is the human-computer interface’s (HMI) key technology. Due to the close coupling of human and machine, wheelchair-type control modes (combination of motion intentions and switches of different actions) are not applicable. For example, the right/left-hand motor imagery (MI) controls the exoskeleton to move forward/stop. There is a mismatch between MI-based control instructions and actual actions because the forward process alternates between left and right. Therefore, this study aims to design a reasonable HMI for a lower limb exoskeleton assistance system and to prove its rationality and effectiveness. We have designed a new HMI combining the human walking process and multimodal biosignals. Experimental data were collected from 14 subjects. We use feature-based and end-to-end analysis methods to analyze multimode signals. In a feature-based analysis, the advantage of using sEMG to distinguish between different movements does not apply to distinguishing different magnitudes of the same movement. A time-frequency subband selection method based on difference patterns is designed for EEG signals in the feature-based analysis. The fusion of EEG and sEMG is implemented to complement each other. The average recognition rate of the multimodal HMI was evaluated to be up to 89.5%, which is 12% and 7.8% higher than that of EEG and sEMG, respectively. Based on end-to-end analysis, a time-domain translation strategy was used to expand the sample and obtain a cross-subject identification rate of 83.6%. All the results indicate that the designed HMI is rational and effective, and there exists potential for practical application.