Development of a fuzzy logic based intelligent system for autonomous guidance of post-stroke rehabilitation exercise.
Identifieur interne : 000939 ( PubMed/Checkpoint ); précédent : 000938; suivant : 000940Development of a fuzzy logic based intelligent system for autonomous guidance of post-stroke rehabilitation exercise.
Auteurs : Rajibul Huq ; Rosalie Wang ; Elaine Lu ; Debbie Hebert ; Hervé Lacheray ; Alex MihailidisSource :
- IEEE ... International Conference on Rehabilitation Robotics : [proceedings] [ 1945-7901 ] ; 2013.
English descriptors
- KwdEn :
- MESH :
- instrumentation : Exercise Therapy, Robotics, Signal Processing, Computer-Assisted.
- methods : Exercise Therapy.
- physiopathology : Arm.
- rehabilitation : Stroke.
- Adult, Female, Fuzzy Logic, Humans, Male, Young Adult.
Abstract
This paper presents preliminary studies in developing a fuzzy logic based intelligent system for autonomous post-stroke upper-limb rehabilitation exercise. The intelligent system autonomously varies control parameters to generate different haptic effects on the robotic device. The robotic device is able to apply both resistive and assistive forces for guiding the patient during the exercise. The fuzzy logic based decision-making system estimates muscle fatigue of the patient using exercise performance and generates a combination of resistive and assistive forces so that the stroke survivor can exercise for longer durations with increasing control. The fuzzy logic based system is initially developed using a study with healthy subjects and preliminary results are also presented to validate the developed system with healthy subjects. The next stage of this work will collect data from stroke survivors for further development of the system.
DOI: 10.1109/ICORR.2013.6650472
PubMed: 24187289
Affiliations:
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pubmed:24187289Le document en format XML
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International Conference on Rehabilitation Robotics : [proceedings]</title><idno type="eISSN">1945-7901</idno><imprint><date when="2013" type="published">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adult</term><term>Arm (physiopathology)</term><term>Exercise Therapy (instrumentation)</term><term>Exercise Therapy (methods)</term><term>Female</term><term>Fuzzy Logic</term><term>Humans</term><term>Male</term><term>Robotics (instrumentation)</term><term>Signal Processing, Computer-Assisted (instrumentation)</term><term>Stroke (rehabilitation)</term><term>Young Adult</term></keywords><keywords scheme="MESH" qualifier="instrumentation" xml:lang="en"><term>Exercise Therapy</term><term>Robotics</term><term>Signal Processing, Computer-Assisted</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Exercise Therapy</term></keywords><keywords scheme="MESH" qualifier="physiopathology" xml:lang="en"><term>Arm</term></keywords><keywords scheme="MESH" qualifier="rehabilitation" xml:lang="en"><term>Stroke</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adult</term><term>Female</term><term>Fuzzy Logic</term><term>Humans</term><term>Male</term><term>Young Adult</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This paper presents preliminary studies in developing a fuzzy logic based intelligent system for autonomous post-stroke upper-limb rehabilitation exercise. The intelligent system autonomously varies control parameters to generate different haptic effects on the robotic device. The robotic device is able to apply both resistive and assistive forces for guiding the patient during the exercise. The fuzzy logic based decision-making system estimates muscle fatigue of the patient using exercise performance and generates a combination of resistive and assistive forces so that the stroke survivor can exercise for longer durations with increasing control. The fuzzy logic based system is initially developed using a study with healthy subjects and preliminary results are also presented to validate the developed system with healthy subjects. The next stage of this work will collect data from stroke survivors for further development of the system.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">24187289</PMID><DateCreated><Year>2013</Year><Month>11</Month><Day>04</Day></DateCreated><DateCompleted><Year>2014</Year><Month>09</Month><Day>15</Day></DateCompleted><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1945-7901</ISSN><JournalIssue CitedMedium="Internet"><Volume>2013</Volume><PubDate><Year>2013</Year><Month>Jun</Month></PubDate></JournalIssue><Title>IEEE ... International Conference on Rehabilitation Robotics : [proceedings]</Title><ISOAbbreviation>IEEE Int Conf Rehabil Robot</ISOAbbreviation></Journal><ArticleTitle>Development of a fuzzy logic based intelligent system for autonomous guidance of post-stroke rehabilitation exercise.</ArticleTitle><Pagination><MedlinePgn>6650472</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1109/ICORR.2013.6650472</ELocationID><Abstract><AbstractText>This paper presents preliminary studies in developing a fuzzy logic based intelligent system for autonomous post-stroke upper-limb rehabilitation exercise. The intelligent system autonomously varies control parameters to generate different haptic effects on the robotic device. The robotic device is able to apply both resistive and assistive forces for guiding the patient during the exercise. The fuzzy logic based decision-making system estimates muscle fatigue of the patient using exercise performance and generates a combination of resistive and assistive forces so that the stroke survivor can exercise for longer durations with increasing control. The fuzzy logic based system is initially developed using a study with healthy subjects and preliminary results are also presented to validate the developed system with healthy subjects. The next stage of this work will collect data from stroke survivors for further development of the system.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Huq</LastName><ForeName>Rajibul</ForeName><Initials>R</Initials></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Rosalie</ForeName><Initials>R</Initials></Author><Author ValidYN="Y"><LastName>Lu</LastName><ForeName>Elaine</ForeName><Initials>E</Initials></Author><Author ValidYN="Y"><LastName>Hebert</LastName><ForeName>Debbie</ForeName><Initials>D</Initials></Author><Author ValidYN="Y"><LastName>Lacheray</LastName><ForeName>Hervé</ForeName><Initials>H</Initials></Author><Author ValidYN="Y"><LastName>Mihailidis</LastName><ForeName>Alex</ForeName><Initials>A</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>IEEE Int Conf Rehabil Robot</MedlineTA><NlmUniqueID>101260913</NlmUniqueID><ISSNLinking>1945-7898</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000328">Adult</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D001132">Arm</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000503">physiopathology</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D005081">Exercise Therapy</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000295">instrumentation</QualifierName><QualifierName MajorTopicYN="N" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D005260">Female</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" 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="N" UI="D012371">Robotics</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000295">instrumentation</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D012815">Signal Processing, Computer-Assisted</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000295">instrumentation</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D020521">Stroke</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000534">rehabilitation</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D055815">Young Adult</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2013</Year><Month>11</Month><Day>5</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2013</Year><Month>11</Month><Day>5</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2014</Year><Month>9</Month><Day>16</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="doi">10.1109/ICORR.2013.6650472</ArticleId><ArticleId IdType="pubmed">24187289</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list></list><tree><noCountry><name sortKey="Hebert, Debbie" sort="Hebert, Debbie" uniqKey="Hebert D" first="Debbie" last="Hebert">Debbie Hebert</name><name sortKey="Huq, Rajibul" sort="Huq, Rajibul" uniqKey="Huq R" first="Rajibul" last="Huq">Rajibul Huq</name><name sortKey="Lacheray, Herve" sort="Lacheray, Herve" uniqKey="Lacheray H" first="Hervé" last="Lacheray">Hervé Lacheray</name><name sortKey="Lu, Elaine" sort="Lu, Elaine" uniqKey="Lu E" first="Elaine" last="Lu">Elaine Lu</name><name sortKey="Mihailidis, Alex" sort="Mihailidis, Alex" uniqKey="Mihailidis A" first="Alex" last="Mihailidis">Alex Mihailidis</name><name sortKey="Wang, Rosalie" sort="Wang, Rosalie" uniqKey="Wang R" first="Rosalie" last="Wang">Rosalie Wang</name></noCountry></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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