Hammerstein–Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals
This paper develops a novel approach to characterise muscle force from electromyography (EMG) signals, which are the electric activities generated by muscles. Based on the nonlinear Hammerstein–Wiener model, the first part of this study outlines the estimation of different sub-models to mimic divers...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Biosensors |
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| Online Access: | https://www.mdpi.com/2079-6374/12/2/117 |
| _version_ | 1797482182330023936 |
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| author | Ines Chihi Lilia Sidhom Ernest Nlandu Kamavuako |
| author_facet | Ines Chihi Lilia Sidhom Ernest Nlandu Kamavuako |
| author_sort | Ines Chihi |
| collection | DOAJ |
| description | This paper develops a novel approach to characterise muscle force from electromyography (EMG) signals, which are the electric activities generated by muscles. Based on the nonlinear Hammerstein–Wiener model, the first part of this study outlines the estimation of different sub-models to mimic diverse force profiles. The second part fixes the appropriate sub-models of a multimodel library and computes the contribution of sub-models to estimate the desired force. Based on a pre-existing dataset, the obtained results show the effectiveness of the proposed approach to estimate muscle force from EMG signals with reasonable accuracy. The coefficient of determination ranges from 0.6568 to 0.9754 using the proposed method compared with a range of 0.5060 to 0.9329 using an artificial neural network (ANN), generating significantly different accuracy (<i>p</i> < 0.03). Results imply that the use of multimodel approach can improve the accuracy in proportional control of prostheses. |
| first_indexed | 2024-03-09T22:27:35Z |
| format | Article |
| id | doaj.art-027fa51ca31743f6bbea37e233cbd20b |
| institution | Directory Open Access Journal |
| issn | 2079-6374 |
| language | English |
| last_indexed | 2024-03-09T22:27:35Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biosensors |
| spelling | doaj.art-027fa51ca31743f6bbea37e233cbd20b2023-11-23T19:01:36ZengMDPI AGBiosensors2079-63742022-02-0112211710.3390/bios12020117Hammerstein–Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG SignalsInes Chihi0Lilia Sidhom1Ernest Nlandu Kamavuako2Department of Engineering, Campus Kirchberg, Faculté des Sciences, des Technologies et de Médecine, Université du Luxembourg, 1359 Luxembourg, LuxembourgLaboratory of Energy Applications and Renewable Energy Efficiency (LAPER), El Manar University, Tunis 1068, TunisiaDepartment of Engineering, King’s College London, London WC2R 2LS, UKThis paper develops a novel approach to characterise muscle force from electromyography (EMG) signals, which are the electric activities generated by muscles. Based on the nonlinear Hammerstein–Wiener model, the first part of this study outlines the estimation of different sub-models to mimic diverse force profiles. The second part fixes the appropriate sub-models of a multimodel library and computes the contribution of sub-models to estimate the desired force. Based on a pre-existing dataset, the obtained results show the effectiveness of the proposed approach to estimate muscle force from EMG signals with reasonable accuracy. The coefficient of determination ranges from 0.6568 to 0.9754 using the proposed method compared with a range of 0.5060 to 0.9329 using an artificial neural network (ANN), generating significantly different accuracy (<i>p</i> < 0.03). Results imply that the use of multimodel approach can improve the accuracy in proportional control of prostheses.https://www.mdpi.com/2079-6374/12/2/117electromyography (EMG) signalsHammerstein–Wiener modelmultimodelartificial neural networkmuscle force |
| spellingShingle | Ines Chihi Lilia Sidhom Ernest Nlandu Kamavuako Hammerstein–Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals Biosensors electromyography (EMG) signals Hammerstein–Wiener model multimodel artificial neural network muscle force |
| title | Hammerstein–Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals |
| title_full | Hammerstein–Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals |
| title_fullStr | Hammerstein–Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals |
| title_full_unstemmed | Hammerstein–Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals |
| title_short | Hammerstein–Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals |
| title_sort | hammerstein wiener multimodel approach for fast and efficient muscle force estimation from emg signals |
| topic | electromyography (EMG) signals Hammerstein–Wiener model multimodel artificial neural network muscle force |
| url | https://www.mdpi.com/2079-6374/12/2/117 |
| work_keys_str_mv | AT ineschihi hammersteinwienermultimodelapproachforfastandefficientmuscleforceestimationfromemgsignals AT liliasidhom hammersteinwienermultimodelapproachforfastandefficientmuscleforceestimationfromemgsignals AT ernestnlandukamavuako hammersteinwienermultimodelapproachforfastandefficientmuscleforceestimationfromemgsignals |