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Sensors 2016, 16(8), 1304; doi:10.3390/s16081304

A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions

1
Malaysia Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur 54100, Malaysia
2
Department of Bio-Science and Engineering, College of Systems Engineering and Science, Shibaura Institute of Technology, Fukasaku 307, Saitama-City 337-8570, Japan
3
Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia
*
Author to whom correspondence should be addressed.
Academic Editors: Octavian Adrian Postolache, Alex Casson and Subhas Mukhopadhyay
Received: 5 April 2016 / Revised: 25 May 2016 / Accepted: 27 June 2016 / Published: 17 August 2016
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
View Full-Text   |   Download PDF [959 KB, uploaded 17 August 2016]   |  

Abstract

In recent years, there has been major interest in the exposure to physical therapy during rehabilitation. Several publications have demonstrated its usefulness in clinical/medical and human machine interface (HMI) applications. An automated system will guide the user to perform the training during rehabilitation independently. Advances in engineering have extended electromyography (EMG) beyond the traditional diagnostic applications to also include applications in diverse areas such as movement analysis. This paper gives an overview of the numerous methods available to recognize motion patterns of EMG signals for both isotonic and isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who would like to select the most appropriate methodology in classifying motion patterns, especially during different types of contractions. For feature extraction, the probability density function (PDF) of EMG signals will be the main interest of this study. Following that, a brief explanation of the different methods for pre-processing, feature extraction and classifying EMG signals will be compared in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above. View Full-Text
Keywords: EMG signals; isotonic contractions; isometric contractions; feature extractions; classifications; probability density functions EMG signals; isotonic contractions; isometric contractions; feature extractions; classifications; probability density functions
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MDPI and ACS Style

Nazmi, N.; Abdul Rahman, M.A.; Yamamoto, S.-I.; Ahmad, S.A.; Zamzuri, H.; Mazlan, S.A. A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions. Sensors 2016, 16, 1304.

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