Title: Wavelet-based feature extraction technique for classification of different shoulder girdle motions for high-level upper limb amputees

Authors: Ghaith K. Sharba; Mousa K. Wali; Ali H. Al-Timemy

Addresses: Department of Medical Instrumentation Techniques Engineering, Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq ' Department of Medical Instrumentation Techniques Engineering, Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq ' Biomedical Engineering Department, Al-Khawarizmi College of Engineering, University of Baghdad, Baghdad, Iraq; Centre for Robotics and Neural System (CRNS), Cognitive Institute, Plymouth University, Plymouth PL4 8AA, UK

Abstract: The aim of this study is to suggest a system for classification of seven classes of shoulder girdle motions for high-level upper limb amputees using pattern recognition (PR) system. In the suggested system, the wavelet transform was utilised for feature extraction and extreme learning machine (ELM) and linear discriminant analysis (LDA) were used as classifiers. The data were recorded from six intact-limbed subjects, and four amputees, with eight channels involving five electromyography (EMG) channels and 3-axis accelerometer. The study shows that the suggested pattern recognition system has the ability to classify the shoulder girdle motions with 92.67% classification accuracy for intact-limbed subjects and 87.67% classification accuracy for amputees by combining EMG and accelerometer channels. The outcomes of this study show that non-invasive PR system can help to provide control signals to drive a prosthetic arm for high level upper limb amputees.

Keywords: accelerometer; pattern recognition; ELM classifier; surface electromyography; upper limb amputation; wavelet transform; shoulder girdle.

DOI: 10.1504/IJMEI.2020.10032890

International Journal of Medical Engineering and Informatics, 2020 Vol.12 No.6, pp.609 - 619

Received: 09 Oct 2018
Accepted: 12 Feb 2019

Published online: 06 Nov 2020 *

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