Development of an individualized stable and force-reducing lower-limb exoskeleton.

In this study, an individualized and stable passive-control lower-limb exoskeleton robot was developed. Users' joint angles and the center of pressure (CoP) of one of their soles were input into a convolutional neural network (CNN)-long short-term memory (LSTM) model to evaluate and adjust the exoskeleton control scheme. The CNN-LSTM model predicted the fitness of the control scheme and output the results to the exoskeleton robot, which modified its control parameters accordingly to enhance walking stability.