Generation of synthetic surface electromyography signals under fatigue conditions for varying force inputs using feedback control algorithm
Surface electromyography is a non-invasive technique used for recording the electrical activity of neuromuscular systems. These signals are random, complex and multi-component. There are several techniques to extract information about the force exerted by muscles during any activity. This work attem...
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| Veröffentlicht in: | Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine Journal of engineering in medicine, 2017-11, Vol.231 (11), p.1025-1033 |
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| container_title | Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine |
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| creator | Venugopal, G Deepak, P Ghosh, Diptasree M Ramakrishnan, S |
| description | Surface electromyography is a non-invasive technique used for recording the electrical activity of neuromuscular systems. These signals are random, complex and multi-component. There are several techniques to extract information about the force exerted by muscles during any activity. This work attempts to generate surface electromyography signals for various magnitudes of force under isometric non-fatigue and fatigue conditions using a feedback model. The model is based on existing current distribution, volume conductor relations, the feedback control algorithm for rate coding and generation of firing pattern. The result shows that synthetic surface electromyography signals are highly complex in both non-fatigue and fatigue conditions. Furthermore, surface electromyography signals have higher amplitude and lower frequency under fatigue condition. This model can be used to study the influence of various signal parameters under fatigue and non-fatigue conditions. |
| doi_str_mv | 10.1177/0954411917727307 |
| format | Article |
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These signals are random, complex and multi-component. There are several techniques to extract information about the force exerted by muscles during any activity. This work attempts to generate surface electromyography signals for various magnitudes of force under isometric non-fatigue and fatigue conditions using a feedback model. The model is based on existing current distribution, volume conductor relations, the feedback control algorithm for rate coding and generation of firing pattern. The result shows that synthetic surface electromyography signals are highly complex in both non-fatigue and fatigue conditions. Furthermore, surface electromyography signals have higher amplitude and lower frequency under fatigue condition. This model can be used to study the influence of various signal parameters under fatigue and non-fatigue conditions.</description><identifier>ISSN: 0954-4119</identifier><identifier>EISSN: 2041-3033</identifier><identifier>DOI: 10.1177/0954411917727307</identifier><identifier>PMID: 28830284</identifier><language>eng</language><publisher>London, England: SAGE Publications</publisher><subject>Algorithms ; Conductors ; Control algorithms ; Control systems ; Control theory ; Current distribution ; Electromyography ; Feedback ; Feedback control ; Feedback, Physiological ; Firing pattern ; Hands ; Information processing ; Isometric ; Materials fatigue ; Models, Biological ; Muscle Contraction ; Muscle Fatigue - physiology ; Muscles ; Neural coding ; Signal Processing, Computer-Assisted ; Surface Properties ; Tongue</subject><ispartof>Proceedings of the Institution of Mechanical Engineers. 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Part H, Journal of engineering in medicine</title><addtitle>Proc Inst Mech Eng H</addtitle><description>Surface electromyography is a non-invasive technique used for recording the electrical activity of neuromuscular systems. These signals are random, complex and multi-component. There are several techniques to extract information about the force exerted by muscles during any activity. This work attempts to generate surface electromyography signals for various magnitudes of force under isometric non-fatigue and fatigue conditions using a feedback model. The model is based on existing current distribution, volume conductor relations, the feedback control algorithm for rate coding and generation of firing pattern. The result shows that synthetic surface electromyography signals are highly complex in both non-fatigue and fatigue conditions. Furthermore, surface electromyography signals have higher amplitude and lower frequency under fatigue condition. This model can be used to study the influence of various signal parameters under fatigue and non-fatigue conditions.</description><subject>Algorithms</subject><subject>Conductors</subject><subject>Control algorithms</subject><subject>Control systems</subject><subject>Control theory</subject><subject>Current distribution</subject><subject>Electromyography</subject><subject>Feedback</subject><subject>Feedback control</subject><subject>Feedback, Physiological</subject><subject>Firing pattern</subject><subject>Hands</subject><subject>Information processing</subject><subject>Isometric</subject><subject>Materials fatigue</subject><subject>Models, Biological</subject><subject>Muscle Contraction</subject><subject>Muscle Fatigue - physiology</subject><subject>Muscles</subject><subject>Neural coding</subject><subject>Signal Processing, Computer-Assisted</subject><subject>Surface Properties</subject><subject>Tongue</subject><issn>0954-4119</issn><issn>2041-3033</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kcFu1DAQhi0EotvCnROyxIVLYJxx4viIqlIqVeIC58h1xlmXxF7sBGmfgZfGYQtClTh55Pn-b2QPY68EvBNCqfegGymF0KWuFYJ6wnY1SFEhID5lu61dbf0zdp7zPQAIAe1zdlZ3HULdyR37eU2Bkll8DDw6no9h2dPiLc9rcsYSp4nskuJ8jGMyh_2RZz8GM2W-hoESdyU6rsRtDIPfLJm7mPgPk44-jFtdHD4c1qUk8u8rouHO2G9bpIgnbqYxJr_s5xfsmStmevlwXrCvH6--XH6qbj9f31x-uK2sFLhUBizqtqsbNwAq2dYwqE526FBKgq5tGk0WB8KmQStQy8Y4N6BUrdat1BIv2NuT95Di95Xy0s8-W5omEyiuuRcahRKtgK6gbx6h93FN2_sLJRWqzV8oOFE2xZwTuf6Q_Fy-oBfQb4vqHy-qRF4_iNe7mYa_gT-bKUB1ArIZ6Z-p_xP-AtFenC0</recordid><startdate>20171101</startdate><enddate>20171101</enddate><creator>Venugopal, G</creator><creator>Deepak, P</creator><creator>Ghosh, Diptasree M</creator><creator>Ramakrishnan, S</creator><general>SAGE Publications</general><general>SAGE PUBLICATIONS, INC</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>JG9</scope><scope>JQ2</scope><scope>K9.</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>20171101</creationdate><title>Generation of synthetic surface electromyography signals under fatigue conditions for varying force inputs using feedback control algorithm</title><author>Venugopal, G ; Deepak, P ; Ghosh, Diptasree M ; Ramakrishnan, S</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c413t-a0c396825fd0374620d78483f344e086559ec3de3553c13945affd34769964943</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Algorithms</topic><topic>Conductors</topic><topic>Control algorithms</topic><topic>Control systems</topic><topic>Control theory</topic><topic>Current distribution</topic><topic>Electromyography</topic><topic>Feedback</topic><topic>Feedback control</topic><topic>Feedback, Physiological</topic><topic>Firing pattern</topic><topic>Hands</topic><topic>Information processing</topic><topic>Isometric</topic><topic>Materials fatigue</topic><topic>Models, Biological</topic><topic>Muscle Contraction</topic><topic>Muscle Fatigue - physiology</topic><topic>Muscles</topic><topic>Neural coding</topic><topic>Signal Processing, Computer-Assisted</topic><topic>Surface Properties</topic><topic>Tongue</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Venugopal, G</creatorcontrib><creatorcontrib>Deepak, P</creatorcontrib><creatorcontrib>Ghosh, Diptasree M</creatorcontrib><creatorcontrib>Ramakrishnan, S</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Proceedings of the Institution of Mechanical Engineers. 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These signals are random, complex and multi-component. There are several techniques to extract information about the force exerted by muscles during any activity. This work attempts to generate surface electromyography signals for various magnitudes of force under isometric non-fatigue and fatigue conditions using a feedback model. The model is based on existing current distribution, volume conductor relations, the feedback control algorithm for rate coding and generation of firing pattern. The result shows that synthetic surface electromyography signals are highly complex in both non-fatigue and fatigue conditions. Furthermore, surface electromyography signals have higher amplitude and lower frequency under fatigue condition. 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| ispartof | Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine, 2017-11, Vol.231 (11), p.1025-1033 |
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| subjects | Algorithms Conductors Control algorithms Control systems Control theory Current distribution Electromyography Feedback Feedback control Feedback, Physiological Firing pattern Hands Information processing Isometric Materials fatigue Models, Biological Muscle Contraction Muscle Fatigue - physiology Muscles Neural coding Signal Processing, Computer-Assisted Surface Properties Tongue |
| title | Generation of synthetic surface electromyography signals under fatigue conditions for varying force inputs using feedback control algorithm |
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