Hand motion classification using a multi-channel surface electromyography sensor.
Identifieur interne : 000C27 ( PubMed/Corpus ); précédent : 000C26; suivant : 000C28Hand motion classification using a multi-channel surface electromyography sensor.
Auteurs : Xueyan Tang ; Yunhui Liu ; Congyi Lv ; Dong SunSource :
- Sensors (Basel, Switzerland) [ 1424-8220 ] ; 2012.
English descriptors
- KwdEn :
- MESH :
- instrumentation : Biosensing Techniques, Electromyography.
- methods : Electromyography, Pattern Recognition, Automated.
- physiology : Hand, Movement.
- Algorithms, Artificial Intelligence, Equipment Design, Equipment Failure Analysis, Gestures, Humans.
Abstract
The human hand has multiple degrees of freedom (DOF) for achieving high-dexterity motions. Identifying and replicating human hand motions are necessary to perform precise and delicate operations in many applications, such as haptic applications. Surface electromyography (sEMG) sensors are a low-cost method for identifying hand motions, in addition to the conventional methods that use data gloves and vision detection. The identification of multiple hand motions is challenging because the error rate typically increases significantly with the addition of more hand motions. Thus, the current study proposes two new methods for feature extraction to solve the problem above. The first method is the extraction of the energy ratio features in the time-domain, which are robust and invariant to motion forces and speeds for the same gesture. The second method is the extraction of the concordance correlation features that describe the relationship between every two channels of the multi-channel sEMG sensor system. The concordance correlation features of a multi-channel sEMG sensor system were shown to provide a vast amount of useful information for identification. Furthermore, a new cascaded-structure classifier is also proposed, in which 11 types of hand gestures can be identified accurately using the newly defined features. Experimental results show that the success rate for the identification of the 11 gestures is significantly high.
DOI: 10.3390/s120201130
PubMed: 22438703
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pubmed:22438703Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Hand motion classification using a multi-channel surface electromyography sensor.</title><author><name sortKey="Tang, Xueyan" sort="Tang, Xueyan" uniqKey="Tang X" first="Xueyan" last="Tang">Xueyan Tang</name><affiliation><nlm:affiliation>Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong, China. xytang@mae.cuhk.edu.hk</nlm:affiliation></affiliation></author><author><name sortKey="Liu, Yunhui" sort="Liu, Yunhui" uniqKey="Liu Y" first="Yunhui" last="Liu">Yunhui Liu</name></author><author><name sortKey="Lv, Congyi" sort="Lv, Congyi" uniqKey="Lv C" first="Congyi" last="Lv">Congyi Lv</name></author><author><name sortKey="Sun, Dong" sort="Sun, Dong" uniqKey="Sun D" first="Dong" last="Sun">Dong Sun</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2012">2012</date><idno type="doi">10.3390/s120201130</idno><idno type="RBID">pubmed:22438703</idno><idno type="pmid">22438703</idno><idno type="wicri:Area/PubMed/Corpus">000C27</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Hand motion classification using a multi-channel surface electromyography sensor.</title><author><name sortKey="Tang, Xueyan" sort="Tang, Xueyan" uniqKey="Tang X" first="Xueyan" last="Tang">Xueyan Tang</name><affiliation><nlm:affiliation>Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong, China. xytang@mae.cuhk.edu.hk</nlm:affiliation></affiliation></author><author><name sortKey="Liu, Yunhui" sort="Liu, Yunhui" uniqKey="Liu Y" first="Yunhui" last="Liu">Yunhui Liu</name></author><author><name sortKey="Lv, Congyi" sort="Lv, Congyi" uniqKey="Lv C" first="Congyi" last="Lv">Congyi Lv</name></author><author><name sortKey="Sun, Dong" sort="Sun, Dong" uniqKey="Sun D" first="Dong" last="Sun">Dong Sun</name></author></analytic><series><title level="j">Sensors (Basel, Switzerland)</title><idno type="eISSN">1424-8220</idno><imprint><date when="2012" type="published">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Artificial Intelligence</term><term>Biosensing Techniques (instrumentation)</term><term>Electromyography (instrumentation)</term><term>Electromyography (methods)</term><term>Equipment Design</term><term>Equipment Failure Analysis</term><term>Gestures</term><term>Hand (physiology)</term><term>Humans</term><term>Movement (physiology)</term><term>Pattern Recognition, Automated (methods)</term></keywords><keywords scheme="MESH" qualifier="instrumentation" xml:lang="en"><term>Biosensing Techniques</term><term>Electromyography</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Electromyography</term><term>Pattern Recognition, Automated</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Hand</term><term>Movement</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Artificial Intelligence</term><term>Equipment Design</term><term>Equipment Failure Analysis</term><term>Gestures</term><term>Humans</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The human hand has multiple degrees of freedom (DOF) for achieving high-dexterity motions. Identifying and replicating human hand motions are necessary to perform precise and delicate operations in many applications, such as haptic applications. Surface electromyography (sEMG) sensors are a low-cost method for identifying hand motions, in addition to the conventional methods that use data gloves and vision detection. The identification of multiple hand motions is challenging because the error rate typically increases significantly with the addition of more hand motions. Thus, the current study proposes two new methods for feature extraction to solve the problem above. The first method is the extraction of the energy ratio features in the time-domain, which are robust and invariant to motion forces and speeds for the same gesture. The second method is the extraction of the concordance correlation features that describe the relationship between every two channels of the multi-channel sEMG sensor system. The concordance correlation features of a multi-channel sEMG sensor system were shown to provide a vast amount of useful information for identification. Furthermore, a new cascaded-structure classifier is also proposed, in which 11 types of hand gestures can be identified accurately using the newly defined features. Experimental results show that the success rate for the identification of the 11 gestures is significantly high.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">22438703</PMID><DateCreated><Year>2012</Year><Month>03</Month><Day>22</Day></DateCreated><DateCompleted><Year>2012</Year><Month>07</Month><Day>20</Day></DateCompleted><DateRevised><Year>2015</Year><Month>02</Month><Day>25</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1424-8220</ISSN><JournalIssue CitedMedium="Internet"><Volume>12</Volume><Issue>2</Issue><PubDate><Year>2012</Year></PubDate></JournalIssue><Title>Sensors (Basel, Switzerland)</Title><ISOAbbreviation>Sensors (Basel)</ISOAbbreviation></Journal><ArticleTitle>Hand motion classification using a multi-channel surface electromyography sensor.</ArticleTitle><Pagination><MedlinePgn>1130-47</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.3390/s120201130</ELocationID><Abstract><AbstractText>The human hand has multiple degrees of freedom (DOF) for achieving high-dexterity motions. Identifying and replicating human hand motions are necessary to perform precise and delicate operations in many applications, such as haptic applications. Surface electromyography (sEMG) sensors are a low-cost method for identifying hand motions, in addition to the conventional methods that use data gloves and vision detection. The identification of multiple hand motions is challenging because the error rate typically increases significantly with the addition of more hand motions. Thus, the current study proposes two new methods for feature extraction to solve the problem above. The first method is the extraction of the energy ratio features in the time-domain, which are robust and invariant to motion forces and speeds for the same gesture. The second method is the extraction of the concordance correlation features that describe the relationship between every two channels of the multi-channel sEMG sensor system. The concordance correlation features of a multi-channel sEMG sensor system were shown to provide a vast amount of useful information for identification. Furthermore, a new cascaded-structure classifier is also proposed, in which 11 types of hand gestures can be identified accurately using the newly defined features. Experimental results show that the success rate for the identification of the 11 gestures is significantly high.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Tang</LastName><ForeName>Xueyan</ForeName><Initials>X</Initials><AffiliationInfo><Affiliation>Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong, China. xytang@mae.cuhk.edu.hk</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Liu</LastName><ForeName>Yunhui</ForeName><Initials>Y</Initials></Author><Author ValidYN="Y"><LastName>Lv</LastName><ForeName>Congyi</ForeName><Initials>C</Initials></Author><Author ValidYN="Y"><LastName>Sun</LastName><ForeName>Dong</ForeName><Initials>D</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2012</Year><Month>01</Month><Day>30</Day></ArticleDate></Article><MedlineJournalInfo><Country>Switzerland</Country><MedlineTA>Sensors (Basel)</MedlineTA><NlmUniqueID>101204366</NlmUniqueID><ISSNLinking>1424-8220</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>Med Eng Phys. 1999 Jun;21(5):303-11</RefSource><PMID Version="1">10576421</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Electromyogr Kinesiol. 2003 Feb;13(1):63-71</RefSource><PMID Version="1">12488088</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Crit Rev Biomed Eng. 2002;30(4-6):459-85</RefSource><PMID Version="1">12739757</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>IEEE Trans Biomed Eng. 1982 Jun;29(6):403-12</RefSource><PMID Version="1">7106790</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>IEEE Trans Biomed Eng. 1986 Dec;33(12):1173-81</RefSource><PMID Version="1">3817851</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Biometrics. 1989 Mar;45(1):255-68</RefSource><PMID Version="1">2720055</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Rehabil Res Dev. 2011;48(6):719-37</RefSource><PMID Version="1">21938658</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>IEEE Trans Biomed Eng. 1995 Jun;42(6):621-31</RefSource><PMID Version="1">7790019</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Neuroimage. 1999 Sep;10(3 Pt 1):282-303</RefSource><PMID Version="1">10458943</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Biol Cybern. 2009 Jan;100(1):35-47</RefSource><PMID Version="1">19015872</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Physiol Paris. 2009 Sep-Dec;103(3-5):255-62</RefSource><PMID Version="1">19665563</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>IEEE Trans Biomed Eng. 2011 Mar;58(3):681-8</RefSource><PMID Version="1">20729161</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>IEEE Trans Neural Syst Rehabil Eng. 2011 Jun;19(3):260-70</RefSource><PMID Version="1">21292599</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>IEEE Trans Biomed Eng. 1993 Jan;40(1):82-94</RefSource><PMID Version="1">8468080</PMID></CommentsCorrections></CommentsCorrectionsList><MeshHeadingList><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D000465">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D001185">Artificial Intelligence</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D015374">Biosensing Techniques</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000295">instrumentation</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D004576">Electromyography</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000295">instrumentation</QualifierName><QualifierName MajorTopicYN="Y" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D004867">Equipment Design</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D019544">Equipment Failure Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D005868">Gestures</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D006225">Hand</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000502">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D006801">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D009068">Movement</DescriptorName><QualifierName 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