Metric Learning for Robust Gait Phase Recognition for a Lower Limb Exoskeleton Robot Based on sEMG

Abstract

This study aims to develop a robust myoelectric control method for gait phase recognition in a lower-limb exoskeleton robot. In the proposed method, a metric learning-based temporal convolution network (ML-TCN) was utilized to extract discriminative features of the surface electromyography (sEMG) signals and recognize four gait phases: heel strike (HS), foot flat (FF), heel off (HO), and swing (SW). Vicon validated the effectiveness of the sEMG data-acquisition system. The proposed method acquires significantly more discriminative features than long short-term memory (LSTM) or common temporal convolutional network (TCN). The experimental results show that the proposed model has a higher prediction accuracy and stronger robustness against disturbances in complex terrain. Finally, under the complex terrain of level ground-ramp ascending-ramp descending walking, the proposed model’s accuracy of gait phase recognition is 96.22%, which is better than LSTM’s 91.20%. Noise disturbances of 10%, 20%, 30%, 40%, and 50% were added to the test set. Compared with LSTM, the resistance to disturbances of the proposed method increased by 8.15%, 8.79%, 9.67%, 10.6%, and 10.61%, respectively.