A novel system for automatic classification of upper limb motor function after stroke: an exploratory study.
Identifieur interne : 000210 ( PubMed/Checkpoint ); précédent : 000209; suivant : 000211A novel system for automatic classification of upper limb motor function after stroke: an exploratory study.
Auteurs : Vítor Tedim Cruz [Portugal] ; Virgílio Ferro Bento [Portugal] ; David Dieteren Ribeiro [Portugal] ; Isabel Araújo [Portugal] ; Catarina Aguiar Branco [Portugal] ; Paula Coutinho [Portugal]Source :
- Medical engineering & physics [ 1873-4030 ] ; 2014.
Descripteurs français
- KwdFr :
- Accident vasculaire cérébral (diagnostic), Accident vasculaire cérébral (physiopathologie), Activité motrice, Adulte, Adulte d'âge moyen, Algorithmes, Conception d'appareillage, Facteurs temps, Humains, Indice de gravité médicale, Membre supérieur (physiopathologie), Mâle, Phénomènes biomécaniques, Reconnaissance automatique des formes (), Soins ambulatoires (), Sujet âgé, Technologie sans fil (instrumentation).
- MESH :
- diagnostic : Accident vasculaire cérébral.
- physiopathologie : Accident vasculaire cérébral, Membre supérieur.
- instrumentation : Activité motrice, Adulte, Adulte d'âge moyen, Algorithmes, Conception d'appareillage, Facteurs temps, Humains, Indice de gravité médicale, Mâle, Phénomènes biomécaniques, Reconnaissance automatique des formes, Soins ambulatoires, Sujet âgé, Technologie sans fil.
English descriptors
- KwdEn :
- Adult, Aged, Algorithms, Ambulatory Care (methods), Biomechanical Phenomena, Equipment Design, Humans, Male, Middle Aged, Motor Activity, Pattern Recognition, Automated (methods), Severity of Illness Index, Stroke (diagnosis), Stroke (physiopathology), Time Factors, Upper Extremity (physiopathology), Wireless Technology (instrumentation).
- MESH :
- diagnosis : Stroke.
- instrumentation : Wireless Technology.
- methods : Ambulatory Care, Pattern Recognition, Automated.
- physiopathology : Stroke, Upper Extremity.
- Adult, Aged, Algorithms, Biomechanical Phenomena, Equipment Design, Humans, Male, Middle Aged, Motor Activity, Severity of Illness Index, Time Factors.
Abstract
In the early post-stroke phase, when clinicians attempt to evaluate interventions and accurately measure motor performance, reliable tools are needed. Therefore, the development of a system capable of independent, repeated and automatic assessment of motor function is of increased importance. This manuscript explores the potential of a newly designed device for automatic assessment of motor impairment after stroke. A portable motion capture system was developed to acquire three-dimensional kinematics data of upper limb movements. These were then computed through an automatic decision tree classifier, with features inferred from the Functional Ability Score (FAS) of the Wolf Motor Function Test (WMFT). Five stroke patients were tested on both sides across five selected tasks. The system was compared against a trained clinician, operating simultaneously and blinded. Regarding performance time, the mean difference (system vs clinician) was 0.17s (sd=0.14s). For FAS evaluation, there was agreement in 4 out of 5 patients in the two tasks evaluated. The prototype tested was able to automatically classify upper limb movement, according to a widely used functional motor scale (WMFT) in a relevant clinical setting. These results represent an important step towards a system capable of precise and independent motor evaluation after stroke. The portability and low-cost design will contribute for its usability in ambulatory clinical settings and research trials.
DOI: 10.1016/j.medengphy.2014.09.009
PubMed: 25280582
Affiliations:
Links toward previous steps (curation, corpus...)
Links to Exploration step
pubmed:25280582Le document en format XML
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Electronic address: virbento@gmail.com.</nlm:affiliation><country xml:lang="fr">Portugal</country><wicri:regionArea>Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro, Portugal; University Institute of Maia - ISMAI, Maia</wicri:regionArea><wicri:noRegion>Maia</wicri:noRegion></affiliation></author><author><name sortKey="Ribeiro, David Dieteren" sort="Ribeiro, David Dieteren" uniqKey="Ribeiro D" first="David Dieteren" last="Ribeiro">David Dieteren Ribeiro</name><affiliation wicri:level="1"><nlm:affiliation>Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro, Portugal.</nlm:affiliation><country xml:lang="fr">Portugal</country><wicri:regionArea>Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro</wicri:regionArea><wicri:noRegion>University of Aveiro</wicri:noRegion></affiliation></author><author><name sortKey="Araujo, Isabel" sort="Araujo, Isabel" uniqKey="Araujo I" first="Isabel" last="Araújo">Isabel Araújo</name><affiliation wicri:level="1"><nlm:affiliation>Neurology Department, Hospital São Sebastião, CHEDV, Santa Maria da Feira, Portugal.</nlm:affiliation><country xml:lang="fr">Portugal</country><wicri:regionArea>Neurology Department, Hospital São Sebastião, CHEDV, Santa Maria da Feira</wicri:regionArea><wicri:noRegion>Santa Maria da Feira</wicri:noRegion></affiliation></author><author><name sortKey="Branco, Catarina Aguiar" sort="Branco, Catarina Aguiar" uniqKey="Branco C" first="Catarina Aguiar" last="Branco">Catarina Aguiar Branco</name><affiliation wicri:level="1"><nlm:affiliation>Rehabilitation Medicine Department, Hospital São Sebastião, CHEDV, Santa Maria da Feira, Portugal.</nlm:affiliation><country xml:lang="fr">Portugal</country><wicri:regionArea>Rehabilitation Medicine Department, Hospital São Sebastião, CHEDV, Santa Maria da Feira</wicri:regionArea><wicri:noRegion>Santa Maria da Feira</wicri:noRegion></affiliation></author><author><name sortKey="Coutinho, Paula" sort="Coutinho, Paula" uniqKey="Coutinho P" first="Paula" last="Coutinho">Paula Coutinho</name><affiliation wicri:level="1"><nlm:affiliation>UnIGENe, Instituto de Biologia Molecular e Celular, University of Porto, Portugal.</nlm:affiliation><country xml:lang="fr">Portugal</country><wicri:regionArea>UnIGENe, Instituto de Biologia Molecular e Celular, University of Porto</wicri:regionArea><wicri:noRegion>University of Porto</wicri:noRegion></affiliation></author></analytic><series><title level="j">Medical engineering & physics</title><idno type="eISSN">1873-4030</idno><imprint><date when="2014" type="published">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adult</term><term>Aged</term><term>Algorithms</term><term>Ambulatory Care (methods)</term><term>Biomechanical Phenomena</term><term>Equipment Design</term><term>Humans</term><term>Male</term><term>Middle Aged</term><term>Motor Activity</term><term>Pattern Recognition, Automated (methods)</term><term>Severity of Illness Index</term><term>Stroke (diagnosis)</term><term>Stroke (physiopathology)</term><term>Time Factors</term><term>Upper Extremity (physiopathology)</term><term>Wireless Technology (instrumentation)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Accident vasculaire cérébral (diagnostic)</term><term>Accident vasculaire cérébral (physiopathologie)</term><term>Activité motrice</term><term>Adulte</term><term>Adulte d'âge moyen</term><term>Algorithmes</term><term>Conception d'appareillage</term><term>Facteurs temps</term><term>Humains</term><term>Indice de gravité médicale</term><term>Membre supérieur (physiopathologie)</term><term>Mâle</term><term>Phénomènes biomécaniques</term><term>Reconnaissance automatique des formes ()</term><term>Soins ambulatoires ()</term><term>Sujet âgé</term><term>Technologie sans fil (instrumentation)</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Stroke</term></keywords><keywords scheme="MESH" qualifier="diagnostic" xml:lang="fr"><term>Accident vasculaire cérébral</term></keywords><keywords scheme="MESH" qualifier="instrumentation" xml:lang="en"><term>Wireless Technology</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Ambulatory Care</term><term>Pattern Recognition, Automated</term></keywords><keywords scheme="MESH" qualifier="physiopathologie" xml:lang="fr"><term>Accident vasculaire cérébral</term><term>Membre supérieur</term></keywords><keywords scheme="MESH" qualifier="physiopathology" xml:lang="en"><term>Stroke</term><term>Upper Extremity</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adult</term><term>Aged</term><term>Algorithms</term><term>Biomechanical Phenomena</term><term>Equipment Design</term><term>Humans</term><term>Male</term><term>Middle Aged</term><term>Motor Activity</term><term>Severity of Illness Index</term><term>Time Factors</term></keywords><keywords scheme="MESH" qualifier="instrumentation" xml:lang="fr"><term>Activité motrice</term><term>Adulte</term><term>Adulte d'âge moyen</term><term>Algorithmes</term><term>Conception d'appareillage</term><term>Facteurs temps</term><term>Humains</term><term>Indice de gravité médicale</term><term>Mâle</term><term>Phénomènes biomécaniques</term><term>Reconnaissance automatique des formes</term><term>Soins ambulatoires</term><term>Sujet âgé</term><term>Technologie sans fil</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In the early post-stroke phase, when clinicians attempt to evaluate interventions and accurately measure motor performance, reliable tools are needed. Therefore, the development of a system capable of independent, repeated and automatic assessment of motor function is of increased importance. This manuscript explores the potential of a newly designed device for automatic assessment of motor impairment after stroke. A portable motion capture system was developed to acquire three-dimensional kinematics data of upper limb movements. These were then computed through an automatic decision tree classifier, with features inferred from the Functional Ability Score (FAS) of the Wolf Motor Function Test (WMFT). Five stroke patients were tested on both sides across five selected tasks. The system was compared against a trained clinician, operating simultaneously and blinded. Regarding performance time, the mean difference (system vs clinician) was 0.17s (sd=0.14s). For FAS evaluation, there was agreement in 4 out of 5 patients in the two tasks evaluated. The prototype tested was able to automatically classify upper limb movement, according to a widely used functional motor scale (WMFT) in a relevant clinical setting. These results represent an important step towards a system capable of precise and independent motor evaluation after stroke. The portability and low-cost design will contribute for its usability in ambulatory clinical settings and research trials.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">25280582</PMID><DateCreated><Year>2014</Year><Month>11</Month><Day>29</Day></DateCreated><DateCompleted><Year>2015</Year><Month>07</Month><Day>24</Day></DateCompleted><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1873-4030</ISSN><JournalIssue CitedMedium="Internet"><Volume>36</Volume><Issue>12</Issue><PubDate><Year>2014</Year><Month>Dec</Month></PubDate></JournalIssue><Title>Medical engineering & physics</Title><ISOAbbreviation>Med Eng Phys</ISOAbbreviation></Journal><ArticleTitle>A novel system for automatic classification of upper limb motor function after stroke: an exploratory study.</ArticleTitle><Pagination><MedlinePgn>1704-10</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.medengphy.2014.09.009</ELocationID><ELocationID EIdType="pii" ValidYN="Y">S1350-4533(14)00243-4</ELocationID><Abstract><AbstractText>In the early post-stroke phase, when clinicians attempt to evaluate interventions and accurately measure motor performance, reliable tools are needed. Therefore, the development of a system capable of independent, repeated and automatic assessment of motor function is of increased importance. This manuscript explores the potential of a newly designed device for automatic assessment of motor impairment after stroke. A portable motion capture system was developed to acquire three-dimensional kinematics data of upper limb movements. These were then computed through an automatic decision tree classifier, with features inferred from the Functional Ability Score (FAS) of the Wolf Motor Function Test (WMFT). Five stroke patients were tested on both sides across five selected tasks. The system was compared against a trained clinician, operating simultaneously and blinded. Regarding performance time, the mean difference (system vs clinician) was 0.17s (sd=0.14s). For FAS evaluation, there was agreement in 4 out of 5 patients in the two tasks evaluated. The prototype tested was able to automatically classify upper limb movement, according to a widely used functional motor scale (WMFT) in a relevant clinical setting. These results represent an important step towards a system capable of precise and independent motor evaluation after stroke. The portability and low-cost design will contribute for its usability in ambulatory clinical settings and research trials.</AbstractText><CopyrightInformation>Copyright © 2014 IPEM. Published by Elsevier Ltd. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Tedim Cruz</LastName><ForeName>Vítor</ForeName><Initials>V</Initials><AffiliationInfo><Affiliation>Neurology Department, Hospital São Sebastião, CHEDV, Santa Maria da Feira, Portugal; Clinical Research Office, Health Sciences Department (SACS), University of Aveiro, Portugal.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Bento</LastName><ForeName>Virgílio Ferro</ForeName><Initials>VF</Initials><AffiliationInfo><Affiliation>Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro, Portugal; University Institute of Maia - ISMAI, Maia, Portugal. Electronic address: virbento@gmail.com.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ribeiro</LastName><ForeName>David Dieteren</ForeName><Initials>DD</Initials><AffiliationInfo><Affiliation>Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro, Portugal.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Araújo</LastName><ForeName>Isabel</ForeName><Initials>I</Initials><AffiliationInfo><Affiliation>Neurology Department, Hospital São Sebastião, CHEDV, Santa Maria da Feira, Portugal.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Branco</LastName><ForeName>Catarina Aguiar</ForeName><Initials>CA</Initials><AffiliationInfo><Affiliation>Rehabilitation Medicine Department, Hospital São Sebastião, CHEDV, Santa Maria da Feira, Portugal.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Coutinho</LastName><ForeName>Paula</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>UnIGENe, Instituto de Biologia Molecular e Celular, University of Porto, Portugal.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D023362">Evaluation Studies</PublicationType><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2014</Year><Month>10</Month><Day>01</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Med Eng Phys</MedlineTA><NlmUniqueID>9422753</NlmUniqueID><ISSNLinking>1350-4533</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000328">Adult</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000368">Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000465">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000553">Ambulatory Care</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D001696">Biomechanical Phenomena</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D004867">Equipment Design</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="D008875">Middle Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D009043">Motor Activity</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D010363">Pattern Recognition, Automated</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D012720">Severity of Illness Index</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D020521">Stroke</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000175">diagnosis</QualifierName><QualifierName MajorTopicYN="Y" UI="Q000503">physiopathology</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D013997">Time Factors</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D034941">Upper Extremity</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000503">physiopathology</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D059015">Wireless Technology</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000295">instrumentation</QualifierName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Automatic movement quantification</Keyword><Keyword MajorTopicYN="N">Motor assessment</Keyword><Keyword MajorTopicYN="N">Rehabilitation</Keyword><Keyword MajorTopicYN="N">Stroke</Keyword><Keyword MajorTopicYN="N">Upper limb</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2014</Year><Month>1</Month><Day>7</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2014</Year><Month>9</Month><Day>2</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2014</Year><Month>9</Month><Day>7</Day></PubMedPubDate><PubMedPubDate PubStatus="aheadofprint"><Year>2014</Year><Month>10</Month><Day>1</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2014</Year><Month>10</Month><Day>5</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2014</Year><Month>10</Month><Day>5</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2015</Year><Month>7</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">25280582</ArticleId><ArticleId IdType="pii">S1350-4533(14)00243-4</ArticleId><ArticleId IdType="doi">10.1016/j.medengphy.2014.09.009</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Portugal</li></country></list><tree><country name="Portugal"><noRegion><name sortKey="Tedim Cruz, Vitor" sort="Tedim Cruz, Vitor" uniqKey="Tedim Cruz V" first="Vítor" last="Tedim Cruz">Vítor Tedim Cruz</name></noRegion><name sortKey="Araujo, Isabel" sort="Araujo, Isabel" uniqKey="Araujo I" first="Isabel" last="Araújo">Isabel Araújo</name><name sortKey="Bento, Virgilio Ferro" sort="Bento, Virgilio Ferro" uniqKey="Bento V" first="Virgílio Ferro" last="Bento">Virgílio Ferro Bento</name><name sortKey="Branco, Catarina Aguiar" sort="Branco, Catarina Aguiar" uniqKey="Branco C" first="Catarina Aguiar" last="Branco">Catarina Aguiar Branco</name><name sortKey="Coutinho, Paula" sort="Coutinho, Paula" uniqKey="Coutinho P" first="Paula" last="Coutinho">Paula Coutinho</name><name sortKey="Ribeiro, David Dieteren" sort="Ribeiro, David Dieteren" uniqKey="Ribeiro D" first="David Dieteren" last="Ribeiro">David Dieteren Ribeiro</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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