A review of classification techniques of EMG signals during isotonic and isometric contractions
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 r...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Multidisciplinary Digital Publishing Institute
2016
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/54911/ http://psasir.upm.edu.my/id/eprint/54911/1/A%20Review%20of%20Classification%20Techniques%20of%20EMG.pdf |
| _version_ | 1848852664286183424 |
|---|---|
| author | Nazmi, Nurhazimah Abdul Rahman, Mohd Azizi Yamamoto, Shin-Ichiroh Ahmad, Siti Anom Zamzuri, Hairi Mazlan, Saiful Amri |
| author_facet | Nazmi, Nurhazimah Abdul Rahman, Mohd Azizi Yamamoto, Shin-Ichiroh Ahmad, Siti Anom Zamzuri, Hairi Mazlan, Saiful Amri |
| author_sort | Nazmi, Nurhazimah |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T10:41:40Z |
| format | Article |
| id | upm-54911 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:41:40Z |
| publishDate | 2016 |
| publisher | Multidisciplinary Digital Publishing Institute |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-549112018-06-12T02:48:10Z http://psasir.upm.edu.my/id/eprint/54911/ A review of classification techniques of EMG signals during isotonic and isometric contractions Nazmi, Nurhazimah Abdul Rahman, Mohd Azizi Yamamoto, Shin-Ichiroh Ahmad, Siti Anom Zamzuri, Hairi Mazlan, Saiful Amri 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. Multidisciplinary Digital Publishing Institute 2016 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/54911/1/A%20Review%20of%20Classification%20Techniques%20of%20EMG.pdf Nazmi, Nurhazimah and Abdul Rahman, Mohd Azizi and Yamamoto, Shin-Ichiroh and Ahmad, Siti Anom and Zamzuri, Hairi and Mazlan, Saiful Amri (2016) A review of classification techniques of EMG signals during isotonic and isometric contractions. Sensors, 16 (8). pp. 1-28. ISSN 1424-8220 10.3390/s16081304 |
| spellingShingle | Nazmi, Nurhazimah Abdul Rahman, Mohd Azizi Yamamoto, Shin-Ichiroh Ahmad, Siti Anom Zamzuri, Hairi Mazlan, Saiful Amri A review of classification techniques of EMG signals during isotonic and isometric contractions |
| title | A review of classification techniques of EMG signals during isotonic and isometric contractions |
| title_full | A review of classification techniques of EMG signals during isotonic and isometric contractions |
| title_fullStr | A review of classification techniques of EMG signals during isotonic and isometric contractions |
| title_full_unstemmed | A review of classification techniques of EMG signals during isotonic and isometric contractions |
| title_short | A review of classification techniques of EMG signals during isotonic and isometric contractions |
| title_sort | review of classification techniques of emg signals during isotonic and isometric contractions |
| url | http://psasir.upm.edu.my/id/eprint/54911/ http://psasir.upm.edu.my/id/eprint/54911/ http://psasir.upm.edu.my/id/eprint/54911/1/A%20Review%20of%20Classification%20Techniques%20of%20EMG.pdf |