Abnormal Gait Classification Based on Multi-sensors in Lower Limb Rehabilitation Robot
An abnormal gait classification method based on multiple sensors for lower limb rehabilitation robot is pro-posed.This method extracts three features:left-right force curve similarity,leg trajectory curvature and gait symmetry,which are used together with abnormal gait classification labels as input parameters of KNN model to realize abnormal gait classi-fication.A human-computer interactive software for walking training of the lower limb rehabilitation robot is designed and implemented.The sensor information acquisition module is integrated into it,so that the software can synchronously collect real-time information of each sensor when patients used the robot for walking training.In this study,four healthy subjects are recruited to carry out experiments with normal gait,simulated hemiplegic gait and simulated Parkinson gait.
万方数据
维普
