Processing Surface EMG Signals for Exoskeleton Motion Control

Frontiers in Neurorobotics

Chaoyang Chen¹  Stephen Lemos¹  Dawei Chen⁴  Gui Yin⁵  Xiaodong Zhang⁵  Hanzhe Li⁵  Jiangcheng Chen⁶ 

[1] Department of Orthopaedic Surgery and Sport Medicine, Detroit Medical Center, Detroit, MI, United States;Department of Rehabilitation Medicine, First Affiliated Hospital, Fujian Medical University, Fuzhou, China;Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China;Robotic Rehabilitation Laboratory, Department of Biomedical Engineering, Wayne State University, Detroit, MI, United States;Shaanxi Key Laboratory of Intelligent Robots, Xi’an Jiaotong University, Xi’an, China;Shenzhen Academy of Robotics, Shenzhen, China;

关键词: exoskeleton;    gait;    electromyography;    volitional control;    treadmill;    rehabilitation;   

DOI  :  10.3389/fnbot.2020.00040

来源: DOAJ

【 摘 要 】

The surface electromyography (sEMG) signal has been used for volitional control of robotic assistive devices. There are still challenges in improving system performance accuracy and signal processing to remove systematic noise. This study presents procedures and a pilot validation of the EMG-driven speed-control of exoskeleton and integrated treadmill with a goal to provide better interaction between a user and the system. The gait cycle duration (GCD) was extracted from sEMG signals using the autocorrelation algorithm and Bayesian fusion algorithm. GCDs of various walking speeds were then programmed to control the motion speed of exoskeleton robotic system. The performance and efficiency of this sEMG-controlled robotic assistive ambulation system was tested and validated among 6 healthy volunteers. The results demonstrated that the autocorrelation algorithm extracted the GCD from individual muscle contraction. The GCDs of individual muscles had variability between different walking steps under a designated walking speed. Bayesian fusion algorithms processed the GCDs of multiple muscles yielding a final GCD with the least variance. The fused GCD effectively controlled the motion speeds of exoskeleton and treadmill. The higher amplitude of EMG signals with shorter GCD was found during a faster walking speed. The algorithms using fused GCDs and gait stride length yielded trajectory joint motion tracks in a shape of sine curve waveform. The joint angles of the exoskeleton measured by a decoder mounted on the hip turned out to be in sine waveforms. The hip joint motion track of the exoskeleton matched the angles projected by trajectory curve generated by computer algorithms based on the fused GCDs with high agreement. The EMG-driven speed-control provided the human-machine inter-limb coordination mechanisms for an intuitive speed control of the exoskeleton-treadmill system at the user’s intents. Potentially the whole system can be used for gait rehabilitation of incomplete spinal cord hemispheric stroke patients as goal-directed and task-oriented training tool.

【 授权许可】

Unknown