Neural network based identification of hand movements using biomedical signals
This paper proposes a methodology that analysis and classifies the EMG and MMG signals using a linear neural network to control prosthetic members. Finger motions discrimination is the key problem in this study. Thus the emphasis is put on myoelectric signal processing approaches in this paper. The...
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| creator | Amaral, T. G. Dias, O. P. Wolczowski, A. Fernao Pires, V. |
| description | This paper proposes a methodology that analysis and classifies the EMG and MMG signals using a linear neural network to control prosthetic members. Finger motions discrimination is the key problem in this study. Thus the emphasis is put on myoelectric signal processing approaches in this paper. The EMG and MMG signals classification system was established using a linear neural network and it is presented the comparison with the classification based on the LVQ neural network. Experimental results show a promising performance in classification of motions based on both MMG and EMG signals. |
| doi_str_mv | 10.1109/INES.2012.6249816 |
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Experimental results show a promising performance in classification of motions based on both MMG and EMG signals.</description><subject>Biological neural networks</subject><subject>Electromyography</subject><subject>EMG and MMG signal classification</subject><subject>LVQ neural network</subject><subject>Microphones</subject><subject>Muscles</subject><subject>prosthesis system</subject><subject>Prosthetics</subject><subject>Support vector machine classification</subject><issn>1543-9259</issn><issn>2767-9462</issn><isbn>9781467326940</isbn><isbn>1467326941</isbn><isbn>9781467326957</isbn><isbn>9781467326933</isbn><isbn>1467326933</isbn><isbn>146732695X</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVkMtOwzAURM1Loir5AMTGP5Di67eXqCpQqQoLuq_s-KYYmgTFKYi_JxLdMJuzmKNZDCG3wBYAzN2vq9XrgjPgC82ls6DPSOGMBamN4Nopc05m3GhTOqn5xb9OsksyAyVF6bhy16TI-Z1NmQywYkaqCo-DP9AOx-9--KDBZ4w0RezG1KTaj6nvaN_QN99F2vZf2E5Npsecuj0NqW8xTtaB5rTv_CHfkKtmAhYnzsn2cbVdPpebl6f18mFTJsfGEmLUXobgmKkx1taoqJoQgCsbeESwQaFwqmmkAGMkk4ppDAKYYFpY1GJO7v5mEyLuPofU-uFndzpH_AI_iFQ_</recordid><startdate>201206</startdate><enddate>201206</enddate><creator>Amaral, T. G.</creator><creator>Dias, O. P.</creator><creator>Wolczowski, A.</creator><creator>Fernao Pires, V.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201206</creationdate><title>Neural network based identification of hand movements using biomedical signals</title><author>Amaral, T. G. ; Dias, O. P. ; Wolczowski, A. ; Fernao Pires, V.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-1dd6a4bb907cedc875d5fbb1258b2de18b5e395ff43177404506eb31030638e63</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Biological neural networks</topic><topic>Electromyography</topic><topic>EMG and MMG signal classification</topic><topic>LVQ neural network</topic><topic>Microphones</topic><topic>Muscles</topic><topic>prosthesis system</topic><topic>Prosthetics</topic><topic>Support vector machine classification</topic><toplevel>online_resources</toplevel><creatorcontrib>Amaral, T. G.</creatorcontrib><creatorcontrib>Dias, O. P.</creatorcontrib><creatorcontrib>Wolczowski, A.</creatorcontrib><creatorcontrib>Fernao Pires, 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 Xplore</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>Amaral, T. G.</au><au>Dias, O. P.</au><au>Wolczowski, A.</au><au>Fernao Pires, V.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Neural network based identification of hand movements using biomedical signals</atitle><btitle>2012 IEEE 16th International Conference on Intelligent Engineering Systems (INES)</btitle><stitle>INES</stitle><date>2012-06</date><risdate>2012</risdate><spage>125</spage><epage>129</epage><pages>125-129</pages><issn>1543-9259</issn><eissn>2767-9462</eissn><isbn>9781467326940</isbn><isbn>1467326941</isbn><eisbn>9781467326957</eisbn><eisbn>9781467326933</eisbn><eisbn>1467326933</eisbn><eisbn>146732695X</eisbn><abstract>This paper proposes a methodology that analysis and classifies the EMG and MMG signals using a linear neural network to control prosthetic members. Finger motions discrimination is the key problem in this study. Thus the emphasis is put on myoelectric signal processing approaches in this paper. The EMG and MMG signals classification system was established using a linear neural network and it is presented the comparison with the classification based on the LVQ neural network. Experimental results show a promising performance in classification of motions based on both MMG and EMG signals.</abstract><pub>IEEE</pub><doi>10.1109/INES.2012.6249816</doi><tpages>5</tpages></addata></record> |
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| ispartof | 2012 IEEE 16th International Conference on Intelligent Engineering Systems (INES), 2012, p.125-129 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Biological neural networks Electromyography EMG and MMG signal classification LVQ neural network Microphones Muscles prosthesis system Prosthetics Support vector machine classification |
| title | Neural network based identification of hand movements using biomedical signals |
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