TY - GEN
T1 - Investigation of fatigue using different EMG features
AU - Aghamohammadi-Sereshki, Azadeh
AU - Bayazi, Mohammad Javad Darvishi
AU - Ghomsheh, Farhad Tabatabai
AU - Amirabdollahian, Farshid
PY - 2019/7/29
Y1 - 2019/7/29
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85071147096&partnerID=8YFLogxK
U2 - 10.1109/ICORR.2019.8779402
DO - 10.1109/ICORR.2019.8779402
M3 - Conference contribution
C2 - 31374616
AN - SCOPUS:85071147096
T3 - IEEE International Conference on Rehabilitation Robotics
SP - 115
EP - 120
BT - 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR)
PB - Institute of Electrical and Electronics Engineers (IEEE)
T2 - 16th IEEE International Conference on Rehabilitation Robotics, ICORR 2019
Y2 - 24 June 2019 through 28 June 2019
ER -