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Feature reduction using locally linear embedding for classification muscle fatigue

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Title: Feature reduction using locally linear embedding for classification muscle fatigue

Authors: Mohamed Sarillee, M. Hariharan, Anas M. N., Omar M. I., Aishah M. N., Q. W.Oung

Journal: ARPN Journal of Engineering and Applied Sciences

HEC Recognition History
Category From To
Y 2023-07-01 2024-09-30
Y 2022-07-01 2023-06-30
Y 2021-07-01 2022-06-30
X 2020-07-01 2021-06-30

Publisher: Khyber Medical College, Peshawar

Country: Pakistan

Year: 2016

Volume: 11

Issue: 7

Language: English

Keywords: K-NNEMGLLEMMGAMGmuscle fatigue

Categories
Signal Processing (1285) Feature Extraction (31) Biomedical Engineering (2453) Machine Learning (3478) Muscle Physiology (30)

Abstract

The aim of this work was to classify muscle condition (non-fatigue and fatigue) using a mutil-modal system. In order to realize this aim, electromyogram (EMG), mechanomyogram (MMG) and acoustic myogram (AMG) signals were recorded from activated muscle during isometric contraction from 20 healthy volunteers. Sixteen features were extracted from each recorded myograms (EMG, MMG and AMG) and concatenated to form a feature set with 48 features. Feature reduction using Locally Linear Embedding (LLE) was proposed to select best discriminative features to enhance the classification of muscle condition. k-nearest neighbor (k-NN) classifier was used and obtained highest accuracy of 93.50% after applying LLE.

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