The classification of movement intention through machine learning models: the identification of significant time-domain EMG features

Mohd Khairuddin, Ismail and Sidek, Shahrul Na'im and Anwar P.P., Abdul Majeed and Mohd Razman, Mohd Azraai and Ahmad Puzi, Asmarani and Md. Yusof, Hazlina (2021) The classification of movement intention through machine learning models: the identification of significant time-domain EMG features. PeerJ Computer Science, 7. pp. 1-17. ISSN 2376-5992

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Abstract

Electromyography (EMG) signal is one of the extensively utilised biological signals for predicting human motor intention, which is an essential element in human-robot collaboration platforms. Studies on motion intention prediction from EMG signals have often been concentrated on either classification and regression models of muscle activity. In this study, we leverage the information from the EMG signals, to detect the subject’s intentions in generating motion commands for a robot-assisted upper limb rehabilitation platform. The EMG signals are recorded from ten healthy subjects’ biceps muscle, and the movements of the upper limb evaluated are voluntary elbow flexion and extension along the sagittal plane. The signals are filtered through a fifth-order Butterworth filter. A number of features were extracted from the filtered signals namely waveform length (WL), mean absolute value (MAV), root mean square (RMS), standard deviation (SD), minimum (MIN) and maximum (MAX). Several different classifiers viz. Linear Discriminant Analysis (LDA), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and k-Nearest Neighbour (k-NN) were investigated on its efficacy to accurately classify the pre-intention and intention classes based on the significant features identified (MIN and MAX) via Extremely Randomised Tree feature selection technique. It was observed from the present investigation that the DT classifier yielded an excellent classification with a classification accuracy of 100%, 99% and 99% on training, testing and validation dataset, respectively based on the identified features. The findings of the present investigation are non-trivial towards facilitating the rehabilitation phase of patients based on their actual capability and hence, would eventually yield a more active participation from them.

Item Type:	Article (Journal)
Additional Information:	3028/88619
Uncontrolled Keywords:	EMG, Machine learning, Feature extraction, Movement intention, Classification
Subjects:	T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User:	Dr. Shahrul Naim Sidek
Date Deposited:	19 Mar 2021 09:05
Last Modified:	16 Jun 2021 12:43
URI:	http://irep.iium.edu.my/id/eprint/88619

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