Motion Rehabilitation Robot Control Based on Human Posture Information

Abstract

In order to meet the needs of postoperative rehabilitation training of lower limbs, a motion rehabilitation robot control system based on human posture information is proposed in this paper. The functions of active/passive training mode control, movement posture and EMG signal acquisition, WiFi communication, safety protection, etc. of the lower limb rehabilitation robot are realized. The recognition and analysis of the training process are realized by using random forest machine learning algorithm and linear regression algorithm. The experimental results show that in the first row of the confusion matrix of the random forest algorithm, 7316 data are correctly identified as speed $a$ and only one data is incorrectly identified as speed $B$ , which is superior to other algorithms. In conclusion, the developed control and monitoring system of lower limb rehabilitation robot can be portable controlled by Android and can realize intelligent analysis of the training process through the monitoring signals in the training process. At the same time, the random forest algorithm has more advantages than the linear regression algorithm in motion recognition, which is of positive significance to the automatic monitoring and intelligent control of the training process.