University of Hertfordshire

By the same authors

Investigation of fatigue using different EMG features

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Original languageEnglish
Title of host publication2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR)
PublisherIEEE Computer Society
Pages115-120
Number of pages6
ISBN (Electronic)9781728127552
DOIs
Publication statusPublished - 29 Jul 2019
Event16th IEEE International Conference on Rehabilitation Robotics, ICORR 2019 - Toronto, Canada
Duration: 24 Jun 201928 Jun 2019

Publication series

NameIEEE International Conference on Rehabilitation Robotics
Volume2019-June
ISSN (Print)1945-7898
ISSN (Electronic)1945-7901

Conference

Conference16th IEEE International Conference on Rehabilitation Robotics, ICORR 2019
CountryCanada
CityToronto
Period24/06/1928/06/19

Abstract

Rehabilitative exercise for people suffering from upper limb impairments has the potential to improve their neuro-plasticity due to repetitive training. Our study investigates the usefulness of Electroencephalogram and Electromyogram (EMG) signals for incorporation in humanrobot interaction loop. Twenty healthy participants recruited who performed a series of physical and cognitive tasks, with an inherent fatiguing component in those tasks. Here we report observed effects on EMG signals. Participants performed a Biceps curl repetitions using a suitable dumbbell in three phases. In phase 1, the initial weight was set to achieve maximum voluntary contraction (MVC). Phase 2 followed with 80 % MVC and phase 3 had 60% MVC. After each phase, they had a break around 3 minutes. EMG data were acquired from Biceps, Triceps, and Brachioradialis muscles. Different EMG features were explored to inform on muscle fatigue during this interaction. Comparing EMG during the first and last dumbbell of each phase demonstrated that the muscle fatigue had caused an increase in the average power (94% of cases) and amplitude (91%) and a decrease in the mean (80%) and the median frequency (57%) of EMG, which was more noticeable in Biceps. The results from integrated EMG showed a continuous rise in all three muscles which was more pronounced in Biceps muscle. Given these results, we identify EMG average power as the most reliable feature for informing on muscle fatigue.

ID: 17693357