HapticV1, PubMed, Corpus, bibRecord, 001126

Self-adaptive robot training of stroke survivors for continuous tracking movements.

Identifieur interne : 001126 ( PubMed/Corpus ); précédent : 001125; suivant : 001127

Self-adaptive robot training of stroke survivors for continuous tracking movements.

Auteurs : Elena Vergaro ; Maura Casadio ; Valentina Squeri ; Psiche Giannoni ; Pietro Morasso ; Vittorio Sanguineti

Source :

Journal of neuroengineering and rehabilitation [ 1743-0003 ] ; 2010.

RBID : pubmed:20230610

English descriptors

KwdEn :
- Adult, Aged, Artificial Intelligence, Feasibility Studies, Female, Humans, Male, Middle Aged, Robotics (instrumentation), Robotics (methods), Stroke (rehabilitation), Survivors, Therapy, Computer-Assisted (instrumentation), User-Computer Interface.
MESH :
- instrumentation : Robotics, Therapy, Computer-Assisted.
- methods : Robotics.
- rehabilitation : Stroke.
- Adult, Aged, Artificial Intelligence, Feasibility Studies, Female, Humans, Male, Middle Aged, Survivors, User-Computer Interface.

Abstract

Although robot therapy is progressively becoming an accepted method of treatment for stroke survivors, few studies have investigated how to adapt the robot/subject interaction forces in an automatic way. The paper is a feasibility study of a novel self-adaptive robot controller to be applied with continuous tracking movements.

DOI: 10.1186/1743-0003-7-13
PubMed: 20230610

Links to Exploration step

pubmed:20230610

Le document en format XML

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<author><name sortKey="Vergaro, Elena" sort="Vergaro, Elena" uniqKey="Vergaro E" first="Elena" last="Vergaro">Elena Vergaro</name>
<affiliation><nlm:affiliation>Department of Informatics, University of Genoa, Genoa, Italy. elena.vergaro@unige.it</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Casadio, Maura" sort="Casadio, Maura" uniqKey="Casadio M" first="Maura" last="Casadio">Maura Casadio</name>
</author>
<author><name sortKey="Squeri, Valentina" sort="Squeri, Valentina" uniqKey="Squeri V" first="Valentina" last="Squeri">Valentina Squeri</name>
</author>
<author><name sortKey="Giannoni, Psiche" sort="Giannoni, Psiche" uniqKey="Giannoni P" first="Psiche" last="Giannoni">Psiche Giannoni</name>
</author>
<author><name sortKey="Morasso, Pietro" sort="Morasso, Pietro" uniqKey="Morasso P" first="Pietro" last="Morasso">Pietro Morasso</name>
</author>
<author><name sortKey="Sanguineti, Vittorio" sort="Sanguineti, Vittorio" uniqKey="Sanguineti V" first="Vittorio" last="Sanguineti">Vittorio Sanguineti</name>
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<sourceDesc><biblStruct><analytic><title xml:lang="en">Self-adaptive robot training of stroke survivors for continuous tracking movements.</title>
<author><name sortKey="Vergaro, Elena" sort="Vergaro, Elena" uniqKey="Vergaro E" first="Elena" last="Vergaro">Elena Vergaro</name>
<affiliation><nlm:affiliation>Department of Informatics, University of Genoa, Genoa, Italy. elena.vergaro@unige.it</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Casadio, Maura" sort="Casadio, Maura" uniqKey="Casadio M" first="Maura" last="Casadio">Maura Casadio</name>
</author>
<author><name sortKey="Squeri, Valentina" sort="Squeri, Valentina" uniqKey="Squeri V" first="Valentina" last="Squeri">Valentina Squeri</name>
</author>
<author><name sortKey="Giannoni, Psiche" sort="Giannoni, Psiche" uniqKey="Giannoni P" first="Psiche" last="Giannoni">Psiche Giannoni</name>
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<author><name sortKey="Morasso, Pietro" sort="Morasso, Pietro" uniqKey="Morasso P" first="Pietro" last="Morasso">Pietro Morasso</name>
</author>
<author><name sortKey="Sanguineti, Vittorio" sort="Sanguineti, Vittorio" uniqKey="Sanguineti V" first="Vittorio" last="Sanguineti">Vittorio Sanguineti</name>
</author>
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<series><title level="j">Journal of neuroengineering and rehabilitation</title>
<idno type="eISSN">1743-0003</idno>
<imprint><date when="2010" type="published">2010</date>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adult</term>
<term>Aged</term>
<term>Artificial Intelligence</term>
<term>Feasibility Studies</term>
<term>Female</term>
<term>Humans</term>
<term>Male</term>
<term>Middle Aged</term>
<term>Robotics (instrumentation)</term>
<term>Robotics (methods)</term>
<term>Stroke (rehabilitation)</term>
<term>Survivors</term>
<term>Therapy, Computer-Assisted (instrumentation)</term>
<term>User-Computer Interface</term>
</keywords>
<keywords scheme="MESH" qualifier="instrumentation" xml:lang="en"><term>Robotics</term>
<term>Therapy, Computer-Assisted</term>
</keywords>
<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Robotics</term>
</keywords>
<keywords scheme="MESH" qualifier="rehabilitation" xml:lang="en"><term>Stroke</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Adult</term>
<term>Aged</term>
<term>Artificial Intelligence</term>
<term>Feasibility Studies</term>
<term>Female</term>
<term>Humans</term>
<term>Male</term>
<term>Middle Aged</term>
<term>Survivors</term>
<term>User-Computer Interface</term>
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<front><div type="abstract" xml:lang="en">Although robot therapy is progressively becoming an accepted method of treatment for stroke survivors, few studies have investigated how to adapt the robot/subject interaction forces in an automatic way. The paper is a feasibility study of a novel self-adaptive robot controller to be applied with continuous tracking movements.</div>
</front>
</TEI>
<pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">20230610</PMID>
<DateCreated><Year>2010</Year>
<Month>04</Month>
<Day>08</Day>
</DateCreated>
<DateCompleted><Year>2010</Year>
<Month>07</Month>
<Day>13</Day>
</DateCompleted>
<DateRevised><Year>2014</Year>
<Month>12</Month>
<Day>04</Day>
</DateRevised>
<Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1743-0003</ISSN>
<JournalIssue CitedMedium="Internet"><Volume>7</Volume>
<PubDate><Year>2010</Year>
</PubDate>
</JournalIssue>
<Title>Journal of neuroengineering and rehabilitation</Title>
<ISOAbbreviation>J Neuroeng Rehabil</ISOAbbreviation>
</Journal>
<ArticleTitle>Self-adaptive robot training of stroke survivors for continuous tracking movements.</ArticleTitle>
<Pagination><MedlinePgn>13</MedlinePgn>
</Pagination>
<ELocationID EIdType="doi" ValidYN="Y">10.1186/1743-0003-7-13</ELocationID>
<Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Although robot therapy is progressively becoming an accepted method of treatment for stroke survivors, few studies have investigated how to adapt the robot/subject interaction forces in an automatic way. The paper is a feasibility study of a novel self-adaptive robot controller to be applied with continuous tracking movements.</AbstractText>
<AbstractText Label="METHODS" NlmCategory="METHODS">The haptic robot Braccio di Ferro is used, in relation with a tracking task. The proposed control architecture is based on three main modules: 1) a force field generator that combines a non linear attractive field and a viscous field; 2) a performance evaluation module; 3) an adaptive controller. The first module operates in a continuous time fashion; the other two modules operate in an intermittent way and are triggered at the end of the current block of trials. The controller progressively decreases the gain of the force field, within a session, but operates in a non monotonic way between sessions: it remembers the minimum gain achieved in a session and propagates it to the next one, which starts with a block whose gain is greater than the previous one. The initial assistance gains are chosen according to a minimal assistance strategy. The scheme can also be applied with closed eyes in order to enhance the role of proprioception in learning and control.</AbstractText>
<AbstractText Label="RESULTS" NlmCategory="RESULTS">The preliminary results with a small group of patients (10 chronic hemiplegic subjects) show that the scheme is robust and promotes a statistically significant improvement in performance indicators as well as a recalibration of the visual and proprioceptive channels. The results confirm that the minimally assistive, self-adaptive strategy is well tolerated by severely impaired subjects and is beneficial also for less severe patients.</AbstractText>
<AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">The experiments provide detailed information about the stability and robustness of the adaptive controller of robot assistance that could be quite relevant for the design of future large scale controlled clinical trials. Moreover, the study suggests that including continuous movement in the repertoire of training is acceptable also by rather severely impaired subjects and confirms the stabilizing effect of alternating vision/no vision trials already found in previous studies.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Vergaro</LastName>
<ForeName>Elena</ForeName>
<Initials>E</Initials>
<AffiliationInfo><Affiliation>Department of Informatics, University of Genoa, Genoa, Italy. elena.vergaro@unige.it</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Casadio</LastName>
<ForeName>Maura</ForeName>
<Initials>M</Initials>
</Author>
<Author ValidYN="Y"><LastName>Squeri</LastName>
<ForeName>Valentina</ForeName>
<Initials>V</Initials>
</Author>
<Author ValidYN="Y"><LastName>Giannoni</LastName>
<ForeName>Psiche</ForeName>
<Initials>P</Initials>
</Author>
<Author ValidYN="Y"><LastName>Morasso</LastName>
<ForeName>Pietro</ForeName>
<Initials>P</Initials>
</Author>
<Author ValidYN="Y"><LastName>Sanguineti</LastName>
<ForeName>Vittorio</ForeName>
<Initials>V</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>2010</Year>
<Month>03</Month>
<Day>15</Day>
</ArticleDate>
</Article>
<MedlineJournalInfo><Country>England</Country>
<MedlineTA>J Neuroeng Rehabil</MedlineTA>
<NlmUniqueID>101232233</NlmUniqueID>
<ISSNLinking>1743-0003</ISSNLinking>
</MedlineJournalInfo>
<CitationSubset>IM</CitationSubset>
<CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>J Neurosci. 2002 Sep 15;22(18):8297-304</RefSource>
<PMID Version="1">12223584</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Biol Cybern. 1982;45(2):131-42</RefSource>
<PMID Version="1">7138957</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>IEEE Trans Neural Syst Rehabil Eng. 2005 Sep;13(3):311-24</RefSource>
<PMID Version="1">16200755</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Exp Brain Res. 2009 Apr;194(2):219-31</RefSource>
<PMID Version="1">19139867</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Technol Health Care. 2006;14(3):123-42</RefSource>
<PMID Version="1">16971753</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>IEEE Trans Neural Syst Rehabil Eng. 2008 Jun;16(3):286-97</RefSource>
<PMID Version="1">18586608</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Clin Rehabil. 2009 Mar;23(3):217-28</RefSource>
<PMID Version="1">19218297</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>J Rehabil Res Dev. 2006 Mar-Apr;43(2):171-84</RefSource>
<PMID Version="1">16847784</PMID>
</CommentsCorrections>
</CommentsCorrectionsList>
<MeshHeadingList><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000328">Adult</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName MajorTopicYN="N" UI="D000368">Aged</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName MajorTopicYN="Y" UI="D001185">Artificial Intelligence</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName MajorTopicYN="N" UI="D005240">Feasibility Studies</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName MajorTopicYN="N" UI="D005260">Female</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="D012371">Robotics</DescriptorName>
<QualifierName MajorTopicYN="N" UI="Q000295">instrumentation</QualifierName>
<QualifierName MajorTopicYN="Y" UI="Q000379">methods</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName MajorTopicYN="N" UI="D020521">Stroke</DescriptorName>
<QualifierName MajorTopicYN="Y" UI="Q000534">rehabilitation</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName MajorTopicYN="N" UI="D017741">Survivors</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName MajorTopicYN="N" UI="D013813">Therapy, Computer-Assisted</DescriptorName>
<QualifierName MajorTopicYN="Y" UI="Q000295">instrumentation</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName MajorTopicYN="Y" UI="D014584">User-Computer Interface</DescriptorName>
</MeshHeading>
</MeshHeadingList>
<OtherID Source="NLM">PMC2850909</OtherID>
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<PubmedData><History><PubMedPubDate PubStatus="received"><Year>2009</Year>
<Month>4</Month>
<Day>8</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="accepted"><Year>2010</Year>
<Month>3</Month>
<Day>15</Day>
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<PubMedPubDate PubStatus="aheadofprint"><Year>2010</Year>
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Self-adaptive robot training of stroke survivors for continuous tracking movements.

Self-adaptive robot training of stroke survivors for continuous tracking movements.

Source :

English descriptors

Abstract

Links to Exploration step

Le document en format XML

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