Gait Phase Recognition for Soft Exoskeleton Assistance Based on Inertial Sensors.

To improve the control of exoskeleton robot for gait assistance, a gait phase recognition method for exoskeleton assistance based on three inertial sensors is proposed, which includes motion state recognition based on support vector machine (SVM), gait phase recognition relying on long short-term memory (LSTM), and a rule-based method for labeling lower limb gait event. Firstly, we used SVM to identify six motion states: standing, level walking, turning left, turning right, left steering, and right steering. Then, an LSTM model was employed to identify the six phases of the lower limb gait during level walking. The experiments demonstrate that this method achieves accuracy rates of 99.65% and 96.81% for the wearer's motion state recognition and gait phase recognition, respectively, with corresponding ${\mathcal{F}_1}$ scores of 0.9957 and 0.9683.