Citation

Lew, Kai Ling and Sim, Kok Swee and Tan, Shing Chiang and Abas, Fazly Salleh (2022) Biofeedback Upper Limb Assessment Using Electroencephalogram, Electromyographic and Electrocardiographic with Machine Learning in Signal Classification. Engineering Letters, 30 (3). pp. 935-947. ISSN 1816-093X, 1816-0948

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Abstract

Physical disability or arm paralysis is a common symptom for the post-stroke survivor. The upper limb rehabilitation is introduced to improve the motor ability of the upper limb and recovery from stroke. However, the recovery rate of the motor ability upper limb is based on physical condition and therapy performance of a patient (subject). The rehabilitation may require more manpower at a center and is time consuming for a physiotherapist to monitor a patient during rehabilitation without the use of technology. The purpose of research in this paper is to evaluate the condition of subjects using a deep learning model with biosignal devices after virtual reality (VR) upper limb assessment. Fifteen control persons and fifteen post-stroke patients have performed two games under VR upper limb assessment, namely, Touch the Ball, and Stack the Cube. The patients were equipped with an electroencephalogram (EEG), electromyographic (EMG), and electrocardiographic (ECG). The measurements were taken before, during, and after the assessment. A common practice in data handling is that all EEG, EMG and ECG signals are pre-processed to remove noises or to condition data. In this work, all three raw biosignals were collectively represented as images. They were used to train deep learning models (namely, convolutional neural network and long-short term memory) of which the models were used to evaluate the condition of a subject. The classification performance of the deep neural network in classifying the biosignals is highly accurate and precise.

Item Type:	Article
Uncontrolled Keywords:	Biosignals, Rehabilitation, Unreal Engine 4, Virtual Reality
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics > TK7871 Electronics--Materials
Divisions:	Faculty of Engineering and Technology (FET) Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	11 Oct 2022 05:58
Last Modified:	11 Oct 2022 05:58
URII:	http://shdl.mmu.edu.my/id/eprint/10533

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