Neural Networks for Online Classification of Hand and Finger Movements Using Surface EMG signals
Myoelectric signals (MES) are the electrical manifestation of muscular contractions and they can be used to create myoelectric prosthesis which is able to function with the amputee's muscle movements. This signal recorded at the surface of the skin of the forearm has been exploited to provide r...
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| creator | Tsenov, G. Zeghbib, A.H. Palis, F. Shoylev, N. Mladenov, V. |
| description | Myoelectric signals (MES) are the electrical manifestation of muscular contractions and they can be used to create myoelectric prosthesis which is able to function with the amputee's muscle movements. This signal recorded at the surface of the skin of the forearm has been exploited to provide recognition of the movement of the hand and finger movements of healthy subject. The objective of the paper is to describe the identification procedure, based on EMG patterns of forearm activity using various neural networks methods and to make a comparison between different intelligent computational methods of identification, which are used in this work. Then an online algorithm for movement identification and classification that utilises the trained neural networks is presented |
| doi_str_mv | 10.1109/NEUREL.2006.341203 |
| format | Conference Proceeding |
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Then an online algorithm for movement identification and classification that utilises the trained neural networks is presented</description><subject>Computational intelligence</subject><subject>Electrodes</subject><subject>Electromyography</subject><subject>EMG signals</subject><subject>Feature extraction</subject><subject>Fingers</subject><subject>Hand and Finger Movements Identification</subject><subject>Muscles</subject><subject>Neural networks</subject><subject>Signal analysis</subject><subject>Signal processing</subject><subject>Testing</subject><isbn>9781424404322</isbn><isbn>1424404320</isbn><isbn>1424404339</isbn><isbn>9781424404339</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2006</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1jM1OwkAURscYExV5Ad3MC4B3ftoyS0MKmEBJVNZ4Z3pLRsvUzBQNby9GXZycnG_xMXYrYCwEmPuq3DyVy7EEyMdKCwnqjF0LLbUGrZQ5Z0NTTP5byks2TOkNAESRa2EmV-y1okPEllfUf3XxPfGmi3wdWh-IT1tMyTfeYe-7wLuGLzDU_IeZDzuKfNV90p5Cn_gmnRb-fIgNOuLlas6T3wVs0w27aE6i4Z8HbDMrX6aL0XI9f5w-LEdOSt2PiCSKmnIAhTVYNbGZy2ytDFJRG2sxK3TmZJODFcpYRGnJocqyGmXhyKkBu_v99US0_Yh-j_G41UIXwgj1DapVVyw</recordid><startdate>200609</startdate><enddate>200609</enddate><creator>Tsenov, G.</creator><creator>Zeghbib, A.H.</creator><creator>Palis, F.</creator><creator>Shoylev, N.</creator><creator>Mladenov, V.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200609</creationdate><title>Neural Networks for Online Classification of Hand and Finger Movements Using Surface EMG signals</title><author>Tsenov, G. ; Zeghbib, A.H. ; Palis, F. ; Shoylev, N. ; Mladenov, V.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c224t-ee2a1de6003ad0b38b5c5bd39ae7d9bba5745c2f60b139baa2beca355da27cec3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Computational intelligence</topic><topic>Electrodes</topic><topic>Electromyography</topic><topic>EMG signals</topic><topic>Feature extraction</topic><topic>Fingers</topic><topic>Hand and Finger Movements Identification</topic><topic>Muscles</topic><topic>Neural networks</topic><topic>Signal analysis</topic><topic>Signal processing</topic><topic>Testing</topic><toplevel>online_resources</toplevel><creatorcontrib>Tsenov, G.</creatorcontrib><creatorcontrib>Zeghbib, A.H.</creatorcontrib><creatorcontrib>Palis, F.</creatorcontrib><creatorcontrib>Shoylev, N.</creatorcontrib><creatorcontrib>Mladenov, V.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Tsenov, G.</au><au>Zeghbib, A.H.</au><au>Palis, F.</au><au>Shoylev, N.</au><au>Mladenov, V.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Neural Networks for Online Classification of Hand and Finger Movements Using Surface EMG signals</atitle><btitle>2006 8th Seminar on Neural Network Applications in Electrical Engineering</btitle><stitle>NEUREL</stitle><date>2006-09</date><risdate>2006</risdate><spage>167</spage><epage>171</epage><pages>167-171</pages><isbn>9781424404322</isbn><isbn>1424404320</isbn><eisbn>1424404339</eisbn><eisbn>9781424404339</eisbn><abstract>Myoelectric signals (MES) are the electrical manifestation of muscular contractions and they can be used to create myoelectric prosthesis which is able to function with the amputee's muscle movements. This signal recorded at the surface of the skin of the forearm has been exploited to provide recognition of the movement of the hand and finger movements of healthy subject. The objective of the paper is to describe the identification procedure, based on EMG patterns of forearm activity using various neural networks methods and to make a comparison between different intelligent computational methods of identification, which are used in this work. Then an online algorithm for movement identification and classification that utilises the trained neural networks is presented</abstract><pub>IEEE</pub><doi>10.1109/NEUREL.2006.341203</doi><tpages>5</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Computational intelligence Electrodes Electromyography EMG signals Feature extraction Fingers Hand and Finger Movements Identification Muscles Neural networks Signal analysis Signal processing Testing |
| title | Neural Networks for Online Classification of Hand and Finger Movements Using Surface EMG signals |
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