Study Of K-Nearest Neighbour Classification Performance On Fatigue And Non-Fatigue EMG Signal Features

Wan Daud, Wan Mohd Bukhari and Jong, Yun Chien and Mohamed Kassim, Anuar and Tokhi, M. O. (2020) Study Of K-Nearest Neighbour Classification Performance On Fatigue And Non-Fatigue EMG Signal Features. International Journal of Advanced Computer Science and Applications, 11 (8). pp. 41-47. ISSN 2158-107X

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Abstract

For our body to move, the muscle must activate by relaxing and contracting. Muscle activation produces bio-electric signals that can be detected using Electromyography or EMG. The signal produced by the muscle is affected by the type of contraction done by the muscle. The eccentric contraction generating different EMG signals from concentric contraction. EMG signal contains multiple features. These features can be extracted using MATLAB software. This paper focuses on the bicep brachii and brachioradialis in the upper arm and forearm, respectively. The EMG signals are extracted using surface EMG whereby electrical pads are placed onto the surface of the muscle. Features can then be extracted from the EMG signal. This paper will focus on the MAV, VAR, and RMS features of the EMG signal. The features are then classified into eccentric, concentric or isometric contraction. The performance of the K-Nearest Neighbour (KNN) classifier is inconsistent due to the EMG data variabilities. The accuracy varies from one data set to another. However, it is concluded that non-fatigue signal classification accuracy is higher than fatigue signal classification accuracy.

Item Type:	Article
Uncontrolled Keywords:	Dynamic contraction, Electromyography, Feature extraction, K-nearest neigbour classifier, Surface electromyography, EMG Signal
Divisions:	Faculty of Electrical Engineering
Depositing User:	Sabariah Ismail
Date Deposited:	09 Dec 2020 14:25
Last Modified:	09 Dec 2020 14:25
URI:	http://eprints.utem.edu.my/id/eprint/24749
Statistic Details:	View Download Statistic

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