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Sensors (Basel). 2016 Aug 17;16(8). pii: E1304. doi: 10.3390/s16081304.

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

Author information

1
Malaysia Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur 54100, Malaysia. nurhazimah2@live.utm.my.
2
Department of Bio-Science and Engineering, College of Systems Engineering and Science, Shibaura Institute of Technology, Fukasaku 307, Saitama-City 337-8570, Japan. nurhazimah2@live.utm.my.
3
Malaysia Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur 54100, Malaysia. azizi.kl@utm.my.
4
Department of Bio-Science and Engineering, College of Systems Engineering and Science, Shibaura Institute of Technology, Fukasaku 307, Saitama-City 337-8570, Japan. yamashin@sic.shibaura-it.ac.jp.
5
Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia. sanom@upm.edu.my.
6
Malaysia Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur 54100, Malaysia. hairi.kl@utm.my.
7
Malaysia Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur 54100, Malaysia. amri.kl@utm.my.

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.

KEYWORDS:

EMG signals; classifications; feature extractions; isometric contractions; isotonic contractions; probability density functions

PMID:
27548165
PMCID:
PMC5017469
DOI:
10.3390/s16081304
[Indexed for MEDLINE]
Free PMC Article

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