Classification of Different Shoulder Girdle Motions for Prosthesis Control Using a Time-Domain Feature Extraction Technique

Huda M. Radha; Alia K. Abdul Hassan; Ali  H. Al-Timemy

doi:10.14500/aro.11064

Huda M. Radha Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq http://orcid.org/0000-0002-8041-7679
Alia K. Abdul Hassan Department of Computer Science, College of Science, University of Technology, Baghdad, Iraq https://orcid.org/0000-0002-6835-8872
Ali H. Al-Timemy Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad, Iraq https://orcid.org/0000-0003-2738-8896

Keywords: Biosignal analysis, Dimensionality reduction, LDA classifier, Time domain

Abstract

Abstract—The upper limb amputation exerts a significant burden on the amputee, limiting their ability to perform everyday activities, and degrading their quality of life. Amputee patients’ quality of life can be improved if they have natural control over their prosthetic hands. Among the biological signals, most commonly used to predict upper limb motor intentions, surface electromyography (sEMG), and axial acceleration sensor signals are essential components of shoulder-level upper limb prosthetic hand control systems. In this work, a pattern recognition system is proposed to create a plan for categorizing high-level upper limb prostheses in seven various types of shoulder girdle motions. Thus, combining seven feature groups, which are root mean square, four-order autoregressive, wavelength, slope sign change, zero crossing (ZC), mean absolute value, and cardinality. In this article, the time-domain features were first extracted from the EMG and acceleration signals. Then, the spectral regression (SR) and principal component analysis dimensionality reduction methods are employed to identify the most salient features, which are then passed to the linear discriminant analysis (LDA) classifier. EMG and axial acceleration signal datasets from six intact-limbed and four amputee participants exhibited an average classification error of 15.68 % based on SR dimensionality reduction using the LDA classifier.

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Author Biographies

Huda M. Radha, Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq

Huda M. Ridha is a Lecturer at the Department of Computer Science, College of Science, University of Baghdad. She got a B.Sc. and M.Sc. degrees in Computer Science. Her research interests are in Artificial Intelligent, Image Processing, Data Mining and Bio-signals analysis.

Alia K. Abdul Hassan, Department of Computer Science, College of Science, University of Technology, Baghdad, Iraq

Alia K. Abdul Hassan is a Professor at the Department of Computer Science, University of Technology, Baghdad, Iraq. She got her B.Sc. degree in Computer Science, the M.Sc. degree in Computer Science and the Ph.D. degree in Computer Science. Her research interests are in Artificial Intelligent, Soft computing, Data Mining, Software engineering, Electronic Management, and Computer security .

Ali H. Al-Timemy, Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad, Iraq

Ali H. Al-Timemy is a Assistant Prof. at the Department of Biomedical Engineering, College of Al-Khwarizmi of Engineering, University of Baghdad, Iraq. He got his B.Sc. degree in Biomedical Engineering, the M.Sc. degree in Biomedical Engineering and Ph.D. degree in Biomedical Engineering] from the Centre for Robotics and Neural Systems (CRNS), Plymouth University, UK. . is research interests are in Bio-medical signal, Image processing, Ppattern recognition, Artificial intelligence and Machine Learning.

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Classification of Different Shoulder Girdle Motions for Prosthesis Control Using a Time-Domain Feature Extraction Technique

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