Author/Authors :
Gao, Baofeng Key Laboratory of Convergence Medical Engineering System and Healthcare Technology - The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, China , Wei, Chao Key Laboratory of Convergence Medical Engineering System and Healthcare Technology - The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, China , Ma, Hongdao Key Laboratory of Convergence Medical Engineering System and Healthcare Technology - The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, China , Yang, Shu Key Laboratory of Convergence Medical Engineering System and Healthcare Technology - The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, China , Ma, Xu Tianjin Key Laboratory for Control Theory & Applications in Complicated System - The School of Electrical and Electronics Engineering, Tianjin University of Technology, Tianjin, China , Zhang, Songyuan State Key Laboratory of Robotics and System -Harbin Institute of Technology, Harbin, China
Abstract :
As an important branch of medical robotics, a rehabilitation training robot for the hemiplegic upper limbs is a research hotspot of rehabilitation training. Based on the motion relearning program, rehabilitation technology, human anatomy, mechanics, computer science, robotics, and other fields of technology are covered. Based on an sEMG real-time training system for rehabilitation, the exoskeleton robot still has some problems that need to be solved in this field. Most of the existing rehabilitation exoskeleton robotic systems are heavy, and it is difficult to ensure the accuracy and real-time performance of sEMG signals. In this paper, we design a real-time training system for the upper limb exoskeleton robot based on the EMG signal. It has four main characteristics: light weight, portability, high precision, and low delay. This work includes the structure of the rehabilitation robotic system and the method of signal processing of the sEMG. An experiment on the accuracy and time delay of the sEMG signal processing has been done. In the experimental results, the recognition accuracy of the sEMG is 94%, and the average delay time is 300 ms, which meets the accuracy and real-time requirements.
