TELKOMNIKA Telecommunication, Computing, Electronics and Control
Improved myoelectric pattern recognition of finger movement using rejection-based extreme learning machine

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Improved myoelectric pattern recognition of finger movement using rejection-based extreme learning machine

Extreme learning machine
Finger movement
Hand exoskeleton
Myoelectric pattern recgnition

Myoelectric control system (MCS) had been applied to hand exoskeleton to
improve the human-machine interaction. The current MCS enables the
exoskeleton to move all fingers concurrently for opening and closing hand and
does not consider robustness issue caused by the condition not considered in
the training stage. This study addressed a new MCS employing novel
myoelectric pattern recognition (M-PR) to handle more movements.
Furthermore, a rejection-based radial-basis function extreme learning machine
(RBF-ELM) was proposed to tackle the movements that are not included in
the training stage. The results of the offline experiments showed the RBF-ELM
with rejection mechanism (RBF-ELM-R) outperformed RBF-ELM without
rejection mechanism and other well-known classifiers. In the online
experiments, using 10-trained classes, the M-PR achieved an accuracy of
89.73% and 89.22% using RBF-ELM-R and RBF-ELM, respectively. In the
experiment with 5-trained classes and 5-untrained classes, the M-PR accuracy
was 80.22% and 59.64% using RBF-ELM-R and RBF-ELM, respectively

Khairul Anam, Adel Al-Jumaily

http://journal.uad.ac.id/index.php/TELKOMNIKA

Sep 15, 2020

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