Application of neural network filters for improving an electromyogram signal filtration
Electromyography is a widely applied method for measuring the electrical activity of muscles in medical diagnostics, rehabilitation, and biomechanics. However, electromyography signals often encounter challenges due to noise and interference, complicating their interpretation and analysis. This pape...
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| Format: | Article |
| Language: | English |
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Tomsk Polytechnic University
2024-03-01
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| Series: | Известия Томского политехнического университета: Промышленная кибернетика |
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| Online Access: | https://indcyb.ru/journal/article/view/48/38 |
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| _version_ | 1850121846461038592 |
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| author | Andrey A. Boshlyakov Nikita A. Shilov Maksim I. Zharkov |
| author_facet | Andrey A. Boshlyakov Nikita A. Shilov Maksim I. Zharkov |
| author_sort | Andrey A. Boshlyakov |
| collection | DOAJ |
| description | Electromyography is a widely applied method for measuring the electrical activity of muscles in medical diagnostics, rehabilitation, and biomechanics. However, electromyography signals often encounter challenges due to noise and interference, complicating their interpretation and analysis. This paper proposes a novel approach: filtering electromyography signals using neural networks. This method effectively extracts valuable information from the signals while minimizing the impact of noise and distortion. The proposed approach involves preprocessing the signal, designing a neural network filter architecture, and training it on appropriate datasets. Experimental results demonstrate the superior efficiency of this method compared to traditional analog methods for filtering electromyography signals. The proposed signal processing method finds applications in medical analysis and rehabilitation medicine, particularly in tasks requiring precise handling of electromyography data. The paper further details the development of a data identification system aimed at determining the position of the hand of a manipulator operator and the applied force. |
| format | Article |
| id | doaj-art-ae9932b5c3c54cb492c181302146a595 |
| institution | OA Journals |
| issn | 2949-5407 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Tomsk Polytechnic University |
| record_format | Article |
| series | Известия Томского политехнического университета: Промышленная кибернетика |
| spelling | doaj-art-ae9932b5c3c54cb492c181302146a5952025-08-20T02:34:59ZengTomsk Polytechnic UniversityИзвестия Томского политехнического университета: Промышленная кибернетика2949-54072024-03-0121303710.18799/29495407/2024/1/48Application of neural network filters for improving an electromyogram signal filtrationAndrey A. Boshlyakov0Nikita A. Shilov1Maksim I. Zharkov2Bauman Moscow State Technical University, Moscow, Russian FederationBauman Moscow State Technical University, Moscow, Russian FederationBauman Moscow State Technical University, Moscow, Russian FederationElectromyography is a widely applied method for measuring the electrical activity of muscles in medical diagnostics, rehabilitation, and biomechanics. However, electromyography signals often encounter challenges due to noise and interference, complicating their interpretation and analysis. This paper proposes a novel approach: filtering electromyography signals using neural networks. This method effectively extracts valuable information from the signals while minimizing the impact of noise and distortion. The proposed approach involves preprocessing the signal, designing a neural network filter architecture, and training it on appropriate datasets. Experimental results demonstrate the superior efficiency of this method compared to traditional analog methods for filtering electromyography signals. The proposed signal processing method finds applications in medical analysis and rehabilitation medicine, particularly in tasks requiring precise handling of electromyography data. The paper further details the development of a data identification system aimed at determining the position of the hand of a manipulator operator and the applied force.https://indcyb.ru/journal/article/view/48/38neural networkbiopotentialelectromyographymanipulator |
| spellingShingle | Andrey A. Boshlyakov Nikita A. Shilov Maksim I. Zharkov Application of neural network filters for improving an electromyogram signal filtration Известия Томского политехнического университета: Промышленная кибернетика neural network biopotential electromyography manipulator |
| title | Application of neural network filters for improving an electromyogram signal filtration |
| title_full | Application of neural network filters for improving an electromyogram signal filtration |
| title_fullStr | Application of neural network filters for improving an electromyogram signal filtration |
| title_full_unstemmed | Application of neural network filters for improving an electromyogram signal filtration |
| title_short | Application of neural network filters for improving an electromyogram signal filtration |
| title_sort | application of neural network filters for improving an electromyogram signal filtration |
| topic | neural network biopotential electromyography manipulator |
| url | https://indcyb.ru/journal/article/view/48/38 |
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