Performance of different threshold estimation methods on SEMG wavelet de-noising in prolonged fatigue identification
Surface electromyography (SEMG) signals are widely used in fatigue identification. Fatigue after high intensity exercise and sports training needs to be balanced with rest to allow biochemical reactions during sports activity to return to a normal level. Inadequate rest leads to prolonged fatigue (P...
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2018
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my.upm.eprints.682882019-05-10T08:29:51Z http://psasir.upm.edu.my/id/eprint/68288/ Performance of different threshold estimation methods on SEMG wavelet de-noising in prolonged fatigue identification Jamaluddin, Nurul Fauzani Ahmad, Siti Anom Mohd Noor, Samsul Bahari Wan Hasan, Wan Zuha Shair, Ezreen Farina Surface electromyography (SEMG) signals are widely used in fatigue identification. Fatigue after high intensity exercise and sports training needs to be balanced with rest to allow biochemical reactions during sports activity to return to a normal level. Inadequate rest leads to prolonged fatigue (PF) conditions such as musculoskeletal disorder, unexplained lethargy and performance decrement. Continuous sports training under these conditions may lead to injury. Fatigue identification at this stage is crucial since changes in amplitude and frequency of SEMG may determine whether the player is under normal fatigue (NF) or PF condition. During data collection, there are many interferences and noises which can reduce signal to noise ratio (SNR) of SEMG and affect PF detection. This paper pre-processed SEMG signals using Stationary Wavelet Transform (SWT) 'db' 45 with different threshold (Th) estimation techniques of de-noising such as RigRSURE, HeurSURE, minimax, universal threshold and a new estimation of threshold method which is based on a baseline of SEMG decomposition details. Naïve Bayes classification results using time and frequency features indicate that the new estimation of threshold method have the highest accuracy (98%), compared to RigRSURE (85%), HuerSURE (68%), Universal Threshold (74%) and minimax (76%). IEEE 2018 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/68288/1/Performance%20of%20different%20threshold%20estimation%20methods%20on%20SEMG%20wavelet%20de-noising%20in%20prolonged%20fatigue%20identification.pdf Jamaluddin, Nurul Fauzani and Ahmad, Siti Anom and Mohd Noor, Samsul Bahari and Wan Hasan, Wan Zuha and Shair, Ezreen Farina (2018) Performance of different threshold estimation methods on SEMG wavelet de-noising in prolonged fatigue identification. In: 2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), 3-6 Dec. 2018, Kuching, Sarawak, Malaysia. (pp. 293-296). 10.1109/IECBES.2018.8626599 |
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Surface electromyography (SEMG) signals are widely used in fatigue identification. Fatigue after high intensity exercise and sports training needs to be balanced with rest to allow biochemical reactions during sports activity to return to a normal level. Inadequate rest leads to prolonged fatigue (PF) conditions such as musculoskeletal disorder, unexplained lethargy and performance decrement. Continuous sports training under these conditions may lead to injury. Fatigue identification at this stage is crucial since changes in amplitude and frequency of SEMG may determine whether the player is under normal fatigue (NF) or PF condition. During data collection, there are many interferences and noises which can reduce signal to noise ratio (SNR) of SEMG and affect PF detection. This paper pre-processed SEMG signals using Stationary Wavelet Transform (SWT) 'db' 45 with different threshold (Th) estimation techniques of de-noising such as RigRSURE, HeurSURE, minimax, universal threshold and a new estimation of threshold method which is based on a baseline of SEMG decomposition details. Naïve Bayes classification results using time and frequency features indicate that the new estimation of threshold method have the highest accuracy (98%), compared to RigRSURE (85%), HuerSURE (68%), Universal Threshold (74%) and minimax (76%). |
format |
Conference or Workshop Item |
author |
Jamaluddin, Nurul Fauzani Ahmad, Siti Anom Mohd Noor, Samsul Bahari Wan Hasan, Wan Zuha Shair, Ezreen Farina |
spellingShingle |
Jamaluddin, Nurul Fauzani Ahmad, Siti Anom Mohd Noor, Samsul Bahari Wan Hasan, Wan Zuha Shair, Ezreen Farina Performance of different threshold estimation methods on SEMG wavelet de-noising in prolonged fatigue identification |
author_facet |
Jamaluddin, Nurul Fauzani Ahmad, Siti Anom Mohd Noor, Samsul Bahari Wan Hasan, Wan Zuha Shair, Ezreen Farina |
author_sort |
Jamaluddin, Nurul Fauzani |
title |
Performance of different threshold estimation methods on SEMG wavelet de-noising in prolonged fatigue identification |
title_short |
Performance of different threshold estimation methods on SEMG wavelet de-noising in prolonged fatigue identification |
title_full |
Performance of different threshold estimation methods on SEMG wavelet de-noising in prolonged fatigue identification |
title_fullStr |
Performance of different threshold estimation methods on SEMG wavelet de-noising in prolonged fatigue identification |
title_full_unstemmed |
Performance of different threshold estimation methods on SEMG wavelet de-noising in prolonged fatigue identification |
title_sort |
performance of different threshold estimation methods on semg wavelet de-noising in prolonged fatigue identification |
publisher |
IEEE |
publishDate |
2018 |
url |
http://psasir.upm.edu.my/id/eprint/68288/1/Performance%20of%20different%20threshold%20estimation%20methods%20on%20SEMG%20wavelet%20de-noising%20in%20prolonged%20fatigue%20identification.pdf http://psasir.upm.edu.my/id/eprint/68288/ |
_version_ |
1643839156649984000 |
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13.18916 |