EMG and EPP-integrated human-machine interface between the paralyzed and rehabilitation exoskeleton.
Identifieur interne : 000D25 ( PubMed/Corpus ); précédent : 000D24; suivant : 000D26EMG and EPP-integrated human-machine interface between the paralyzed and rehabilitation exoskeleton.
Auteurs : Yue H. Yin ; Yuan J. Fan ; Li D. XuSource :
- IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society [ 1558-0032 ] ; 2012.
English descriptors
- KwdEn :
- Adult, Algorithms, Artificial Intelligence, Artificial Limbs, Electromyography (instrumentation), Electromyography (methods), Fuzzy Logic, Humans, Male, Man-Machine Systems, Neural Networks (Computer), Paraplegia (rehabilitation), Proprioception (physiology), Robotics (instrumentation), Signal Processing, Computer-Assisted, User-Computer Interface.
- MESH :
- instrumentation : Electromyography, Robotics.
- methods : Electromyography.
- physiology : Proprioception.
- rehabilitation : Paraplegia.
- Adult, Algorithms, Artificial Intelligence, Artificial Limbs, Fuzzy Logic, Humans, Male, Man-Machine Systems, Neural Networks (Computer), Signal Processing, Computer-Assisted, User-Computer Interface.
Abstract
Although a lower extremity exoskeleton shows great prospect in the rehabilitation of the lower limb, it has not yet been widely applied to the clinical rehabilitation of the paralyzed. This is partly caused by insufficient information interactions between the paralyzed and existing exoskeleton that cannot meet the requirements of harmonious control. In this research, a bidirectional human-machine interface including a neurofuzzy controller and an extended physiological proprioception (EPP) feedback system is developed by imitating the biological closed-loop control system of human body. The neurofuzzy controller is built to decode human motion in advance by the fusion of the fuzzy electromyographic signals reflecting human motion intention and the precise proprioception providing joint angular feedback information. It transmits control information from human to exoskeleton, while the EPP feedback system based on haptic stimuli transmits motion information of the exoskeleton back to the human. Joint angle and torque information are transmitted in the form of air pressure to the human body. The real-time bidirectional human-machine interface can help a patient with lower limb paralysis to control the exoskeleton with his/her healthy side and simultaneously perceive motion on the paralyzed side by EPP. The interface rebuilds a closed-loop motion control system for paralyzed patients and realizes harmonious control of the human-machine system.
DOI: 10.1109/TITB.2011.2178034
PubMed: 22249763
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pubmed:22249763Le document en format XML
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Xu</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2012">2012</date><idno type="doi">10.1109/TITB.2011.2178034</idno><idno type="RBID">pubmed:22249763</idno><idno type="pmid">22249763</idno><idno type="wicri:Area/PubMed/Corpus">000D25</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">EMG and EPP-integrated human-machine interface between the paralyzed and rehabilitation exoskeleton.</title><author><name sortKey="Yin, Yue H" sort="Yin, Yue H" uniqKey="Yin Y" first="Yue H" last="Yin">Yue H. Yin</name><affiliation><nlm:affiliation>State Key Laboratory of Mechanical System and Vibration, the Robotics Institute, Shanghai Jiao Tong University, Shanghai 200240, China. yhyin@sjtu.edu.cn</nlm:affiliation></affiliation></author><author><name sortKey="Fan, Yuan J" sort="Fan, Yuan J" uniqKey="Fan Y" first="Yuan J" last="Fan">Yuan J. Fan</name></author><author><name sortKey="Xu, Li D" sort="Xu, Li D" uniqKey="Xu L" first="Li D" last="Xu">Li D. Xu</name></author></analytic><series><title level="j">IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society</title><idno type="eISSN">1558-0032</idno><imprint><date when="2012" type="published">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adult</term><term>Algorithms</term><term>Artificial Intelligence</term><term>Artificial Limbs</term><term>Electromyography (instrumentation)</term><term>Electromyography (methods)</term><term>Fuzzy Logic</term><term>Humans</term><term>Male</term><term>Man-Machine Systems</term><term>Neural Networks (Computer)</term><term>Paraplegia (rehabilitation)</term><term>Proprioception (physiology)</term><term>Robotics (instrumentation)</term><term>Signal Processing, Computer-Assisted</term><term>User-Computer Interface</term></keywords><keywords scheme="MESH" qualifier="instrumentation" xml:lang="en"><term>Electromyography</term><term>Robotics</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Electromyography</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Proprioception</term></keywords><keywords scheme="MESH" qualifier="rehabilitation" xml:lang="en"><term>Paraplegia</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adult</term><term>Algorithms</term><term>Artificial Intelligence</term><term>Artificial Limbs</term><term>Fuzzy Logic</term><term>Humans</term><term>Male</term><term>Man-Machine Systems</term><term>Neural Networks (Computer)</term><term>Signal Processing, Computer-Assisted</term><term>User-Computer Interface</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Although a lower extremity exoskeleton shows great prospect in the rehabilitation of the lower limb, it has not yet been widely applied to the clinical rehabilitation of the paralyzed. This is partly caused by insufficient information interactions between the paralyzed and existing exoskeleton that cannot meet the requirements of harmonious control. In this research, a bidirectional human-machine interface including a neurofuzzy controller and an extended physiological proprioception (EPP) feedback system is developed by imitating the biological closed-loop control system of human body. The neurofuzzy controller is built to decode human motion in advance by the fusion of the fuzzy electromyographic signals reflecting human motion intention and the precise proprioception providing joint angular feedback information. It transmits control information from human to exoskeleton, while the EPP feedback system based on haptic stimuli transmits motion information of the exoskeleton back to the human. Joint angle and torque information are transmitted in the form of air pressure to the human body. The real-time bidirectional human-machine interface can help a patient with lower limb paralysis to control the exoskeleton with his/her healthy side and simultaneously perceive motion on the paralyzed side by EPP. The interface rebuilds a closed-loop motion control system for paralyzed patients and realizes harmonious control of the human-machine system.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">22249763</PMID><DateCreated><Year>2012</Year><Month>07</Month><Day>04</Day></DateCreated><DateCompleted><Year>2012</Year><Month>10</Month><Day>22</Day></DateCompleted><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1558-0032</ISSN><JournalIssue CitedMedium="Internet"><Volume>16</Volume><Issue>4</Issue><PubDate><Year>2012</Year><Month>Jul</Month></PubDate></JournalIssue><Title>IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society</Title><ISOAbbreviation>IEEE Trans Inf Technol Biomed</ISOAbbreviation></Journal><ArticleTitle>EMG and EPP-integrated human-machine interface between the paralyzed and rehabilitation exoskeleton.</ArticleTitle><Pagination><MedlinePgn>542-9</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1109/TITB.2011.2178034</ELocationID><Abstract><AbstractText>Although a lower extremity exoskeleton shows great prospect in the rehabilitation of the lower limb, it has not yet been widely applied to the clinical rehabilitation of the paralyzed. This is partly caused by insufficient information interactions between the paralyzed and existing exoskeleton that cannot meet the requirements of harmonious control. In this research, a bidirectional human-machine interface including a neurofuzzy controller and an extended physiological proprioception (EPP) feedback system is developed by imitating the biological closed-loop control system of human body. The neurofuzzy controller is built to decode human motion in advance by the fusion of the fuzzy electromyographic signals reflecting human motion intention and the precise proprioception providing joint angular feedback information. It transmits control information from human to exoskeleton, while the EPP feedback system based on haptic stimuli transmits motion information of the exoskeleton back to the human. Joint angle and torque information are transmitted in the form of air pressure to the human body. The real-time bidirectional human-machine interface can help a patient with lower limb paralysis to control the exoskeleton with his/her healthy side and simultaneously perceive motion on the paralyzed side by EPP. The interface rebuilds a closed-loop motion control system for paralyzed patients and realizes harmonious control of the human-machine system.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Yin</LastName><ForeName>Yue H</ForeName><Initials>YH</Initials><AffiliationInfo><Affiliation>State Key Laboratory of Mechanical System and Vibration, the Robotics Institute, Shanghai Jiao Tong University, Shanghai 200240, China. yhyin@sjtu.edu.cn</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Fan</LastName><ForeName>Yuan J</ForeName><Initials>YJ</Initials></Author><Author ValidYN="Y"><LastName>Xu</LastName><ForeName>Li D</ForeName><Initials>LD</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>09</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>IEEE Trans Inf Technol Biomed</MedlineTA><NlmUniqueID>9712259</NlmUniqueID><ISSNLinking>1089-7771</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000328">Adult</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000465">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D001185">Artificial Intelligence</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D001186">Artificial Limbs</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D004576">Electromyography</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000295">instrumentation</QualifierName><QualifierName MajorTopicYN="Y" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D017143">Fuzzy Logic</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D006801">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D008297">Male</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D008328">Man-Machine Systems</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D016571">Neural Networks (Computer)</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D010264">Paraplegia</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000534">rehabilitation</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D011434">Proprioception</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000502">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D012371">Robotics</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000295">instrumentation</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D012815">Signal Processing, Computer-Assisted</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D014584">User-Computer Interface</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="aheadofprint"><Year>2012</Year><Month>1</Month><Day>09</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2012</Year><Month>1</Month><Day>18</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2012</Year><Month>1</Month><Day>18</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2012</Year><Month>10</Month><Day>23</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="doi">10.1109/TITB.2011.2178034</ArticleId><ArticleId IdType="pubmed">22249763</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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