Study Of K-Nearest Neighbour Classification Performance On Fatigue And Non-Fatigue EMG Signal Features
For our body to move, the muscle must activate by relaxing and contracting. Muscle activation produces bio-electric signals that can be detected using Electromyography or EMG. The signal produced by the muscle is affected by the type of contraction done by the muscle. The eccentric contraction gener...
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Science and Information Organization
2020
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| Online Access: | http://eprints.utem.edu.my/id/eprint/24749/2/PAPER_6-STUDY_OF_K_NEAREST_NEIGHBOUR_CLASSIFICATION.PDF http://eprints.utem.edu.my/id/eprint/24749/ https://thesai.org/Downloads/Volume11No8/Paper_6-Study_of_K_Nearest_Neighbour_Classification.pdf |
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| author | Wan Daud, Wan Mohd Bukhari Jong, Yun Chien Mohamed Kassim, Anuar Tokhi, M. O. |
| author_facet | Wan Daud, Wan Mohd Bukhari Jong, Yun Chien Mohamed Kassim, Anuar Tokhi, M. O. |
| author_sort | Wan Daud, Wan Mohd Bukhari |
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| content_provider | Universiti Teknikal Malaysia Melaka |
| content_source | UTEM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | For our body to move, the muscle must activate by relaxing and contracting. Muscle activation produces bio-electric signals that can be detected using Electromyography or EMG. The signal produced by the muscle is affected by the type of contraction done by the muscle. The eccentric contraction generating different EMG signals from concentric contraction. EMG signal contains multiple features. These features can be extracted using MATLAB software. This paper focuses on the bicep brachii and brachioradialis in the upper arm and forearm, respectively. The EMG signals are extracted using surface EMG whereby electrical pads are placed onto the surface of the muscle. Features can then be extracted from the EMG signal. This paper will focus on the MAV, VAR, and RMS features of the EMG signal. The features are then classified into eccentric, concentric or isometric contraction. The performance of the K-Nearest Neighbour (KNN) classifier is inconsistent due to the EMG data variabilities. The accuracy varies from one data set to another. However, it is concluded that non-fatigue signal classification accuracy is higher than fatigue signal classification accuracy. |
| format | Article |
| id | my.utem.eprints-24749 |
| institution | Universiti Teknikal Malaysia Melaka |
| language | en |
| publishDate | 2020 |
| publisher | Science and Information Organization |
| record_format | eprints |
| spelling | my.utem.eprints-247492020-12-09T14:25:13Z http://eprints.utem.edu.my/id/eprint/24749/ Study Of K-Nearest Neighbour Classification Performance On Fatigue And Non-Fatigue EMG Signal Features Wan Daud, Wan Mohd Bukhari Jong, Yun Chien Mohamed Kassim, Anuar Tokhi, M. O. For our body to move, the muscle must activate by relaxing and contracting. Muscle activation produces bio-electric signals that can be detected using Electromyography or EMG. The signal produced by the muscle is affected by the type of contraction done by the muscle. The eccentric contraction generating different EMG signals from concentric contraction. EMG signal contains multiple features. These features can be extracted using MATLAB software. This paper focuses on the bicep brachii and brachioradialis in the upper arm and forearm, respectively. The EMG signals are extracted using surface EMG whereby electrical pads are placed onto the surface of the muscle. Features can then be extracted from the EMG signal. This paper will focus on the MAV, VAR, and RMS features of the EMG signal. The features are then classified into eccentric, concentric or isometric contraction. The performance of the K-Nearest Neighbour (KNN) classifier is inconsistent due to the EMG data variabilities. The accuracy varies from one data set to another. However, it is concluded that non-fatigue signal classification accuracy is higher than fatigue signal classification accuracy. Science and Information Organization 2020-09 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/24749/2/PAPER_6-STUDY_OF_K_NEAREST_NEIGHBOUR_CLASSIFICATION.PDF Wan Daud, Wan Mohd Bukhari and Jong, Yun Chien and Mohamed Kassim, Anuar and Tokhi, M. O. (2020) Study Of K-Nearest Neighbour Classification Performance On Fatigue And Non-Fatigue EMG Signal Features. International Journal of Advanced Computer Science and Applications, 11 (8). pp. 41-47. ISSN 2158-107X https://thesai.org/Downloads/Volume11No8/Paper_6-Study_of_K_Nearest_Neighbour_Classification.pdf 10.14569/IJACSA.2020.0110806 |
| spellingShingle | Wan Daud, Wan Mohd Bukhari Jong, Yun Chien Mohamed Kassim, Anuar Tokhi, M. O. Study Of K-Nearest Neighbour Classification Performance On Fatigue And Non-Fatigue EMG Signal Features |
| title | Study Of K-Nearest Neighbour Classification Performance On Fatigue And Non-Fatigue EMG Signal Features |
| title_full | Study Of K-Nearest Neighbour Classification Performance On Fatigue And Non-Fatigue EMG Signal Features |
| title_fullStr | Study Of K-Nearest Neighbour Classification Performance On Fatigue And Non-Fatigue EMG Signal Features |
| title_full_unstemmed | Study Of K-Nearest Neighbour Classification Performance On Fatigue And Non-Fatigue EMG Signal Features |
| title_short | Study Of K-Nearest Neighbour Classification Performance On Fatigue And Non-Fatigue EMG Signal Features |
| title_sort | study of k-nearest neighbour classification performance on fatigue and non-fatigue emg signal features |
| url | http://eprints.utem.edu.my/id/eprint/24749/2/PAPER_6-STUDY_OF_K_NEAREST_NEIGHBOUR_CLASSIFICATION.PDF http://eprints.utem.edu.my/id/eprint/24749/ https://thesai.org/Downloads/Volume11No8/Paper_6-Study_of_K_Nearest_Neighbour_Classification.pdf |
| url_provider | http://eprints.utem.edu.my/ |