Investigation of Fatigue Using Different EMG Features

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 human-robot 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.