AUTHOR=Yin Gui , Zhang Xiaodong , Chen Dawei , Li Hanzhe , Chen Jiangcheng , Chen Chaoyang , Lemos Stephen 

TITLE=Processing Surface EMG Signals for Exoskeleton Motion Control

JOURNAL=Frontiers in Neurorobotics

VOLUME=Volume 14 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2020.00040

DOI=10.3389/fnbot.2020.00040

ISSN=1662-5218

ABSTRACT=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. A sEMG-controlled assistive ambulation exoskeleton was developed in this study, aiming to achieve harmonic interactions between a user and the exoskeleton assistive ambulation system. The gait cycle duration (GCD) was extracted from sEMG signals using the autocorrelation algorithm and Bayesian redundant 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 by wearing the sEMG controlled exoskeleton and walking on a treadmill synchronized to the exoskeleton system. The results demonstrated that the autocorrelation algorithm extracted GCDs from individual EMGs spared of noise and EMG signals from unintended muscle contraction. The GCDs of individual muscles were different between different walking steps under a designated walking speed. Bayesian redundant fusion algorithms processed GCDs of multiple muscles yielding a GCD with the least variance of GCD. 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 based on fused GCDs and gait strike length yielded trajectory joint motion track 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 a track of sine waveform.  The hip joint motion track of the exoskeleton matched the angles projected by hip motion encoding curve produced by computer algorithms based on the fused GCDs with high agreement. GCD-based sEMG-controlled algorithms provided an adaptive interaction control between the exoskeleton assistive ambulation system and the user.