Review of control strategies for robotic movement training after neurologic injury.
Identifieur interne : 001260 ( PubMed/Corpus ); précédent : 001259; suivant : 001261Review of control strategies for robotic movement training after neurologic injury.
Auteurs : Laura Marchal-Crespo ; David J. ReinkensmeyerSource :
- Journal of neuroengineering and rehabilitation [ 1743-0003 ] ; 2009.
English descriptors
- KwdEn :
- MESH :
- methods : Electromyography, Musculoskeletal Manipulations, Robotics.
- rehabilitation : Trauma, Nervous System.
- Algorithms, Humans.
Abstract
There is increasing interest in using robotic devices to assist in movement training following neurologic injuries such as stroke and spinal cord injury. This paper reviews control strategies for robotic therapy devices. Several categories of strategies have been proposed, including, assistive, challenge-based, haptic simulation, and coaching. The greatest amount of work has been done on developing assistive strategies, and thus the majority of this review summarizes techniques for implementing assistive strategies, including impedance-, counterbalance-, and EMG- based controllers, as well as adaptive controllers that modify control parameters based on ongoing participant performance. Clinical evidence regarding the relative effectiveness of different types of robotic therapy controllers is limited, but there is initial evidence that some control strategies are more effective than others. It is also now apparent there may be mechanisms by which some robotic control approaches might actually decrease the recovery possible with comparable, non-robotic forms of training. In future research, there is a need for head-to-head comparison of control algorithms in randomized, controlled clinical trials, and for improved models of human motor recovery to provide a more rational framework for designing robotic therapy control strategies.
DOI: 10.1186/1743-0003-6-20
PubMed: 19531254
Links to Exploration step
pubmed:19531254Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Review of control strategies for robotic movement training after neurologic injury.</title><author><name sortKey="Marchal Crespo, Laura" sort="Marchal Crespo, Laura" uniqKey="Marchal Crespo L" first="Laura" last="Marchal-Crespo">Laura Marchal-Crespo</name><affiliation><nlm:affiliation>Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA. lmarchal@uci.edu</nlm:affiliation></affiliation></author><author><name sortKey="Reinkensmeyer, David J" sort="Reinkensmeyer, David J" uniqKey="Reinkensmeyer D" first="David J" last="Reinkensmeyer">David J. Reinkensmeyer</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2009">2009</date><idno type="doi">10.1186/1743-0003-6-20</idno><idno type="RBID">pubmed:19531254</idno><idno type="pmid">19531254</idno><idno type="wicri:Area/PubMed/Corpus">001260</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Review of control strategies for robotic movement training after neurologic injury.</title><author><name sortKey="Marchal Crespo, Laura" sort="Marchal Crespo, Laura" uniqKey="Marchal Crespo L" first="Laura" last="Marchal-Crespo">Laura Marchal-Crespo</name><affiliation><nlm:affiliation>Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA. lmarchal@uci.edu</nlm:affiliation></affiliation></author><author><name sortKey="Reinkensmeyer, David J" sort="Reinkensmeyer, David J" uniqKey="Reinkensmeyer D" first="David J" last="Reinkensmeyer">David J. Reinkensmeyer</name></author></analytic><series><title level="j">Journal of neuroengineering and rehabilitation</title><idno type="eISSN">1743-0003</idno><imprint><date when="2009" type="published">2009</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Electromyography (methods)</term><term>Humans</term><term>Musculoskeletal Manipulations (methods)</term><term>Robotics (methods)</term><term>Trauma, Nervous System (rehabilitation)</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Electromyography</term><term>Musculoskeletal Manipulations</term><term>Robotics</term></keywords><keywords scheme="MESH" qualifier="rehabilitation" xml:lang="en"><term>Trauma, Nervous System</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Humans</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">There is increasing interest in using robotic devices to assist in movement training following neurologic injuries such as stroke and spinal cord injury. This paper reviews control strategies for robotic therapy devices. Several categories of strategies have been proposed, including, assistive, challenge-based, haptic simulation, and coaching. The greatest amount of work has been done on developing assistive strategies, and thus the majority of this review summarizes techniques for implementing assistive strategies, including impedance-, counterbalance-, and EMG- based controllers, as well as adaptive controllers that modify control parameters based on ongoing participant performance. Clinical evidence regarding the relative effectiveness of different types of robotic therapy controllers is limited, but there is initial evidence that some control strategies are more effective than others. It is also now apparent there may be mechanisms by which some robotic control approaches might actually decrease the recovery possible with comparable, non-robotic forms of training. In future research, there is a need for head-to-head comparison of control algorithms in randomized, controlled clinical trials, and for improved models of human motor recovery to provide a more rational framework for designing robotic therapy control strategies.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">19531254</PMID><DateCreated><Year>2009</Year><Month>07</Month><Day>15</Day></DateCreated><DateCompleted><Year>2009</Year><Month>08</Month><Day>27</Day></DateCompleted><DateRevised><Year>2014</Year><Month>12</Month><Day>07</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1743-0003</ISSN><JournalIssue CitedMedium="Internet"><Volume>6</Volume><PubDate><Year>2009</Year></PubDate></JournalIssue><Title>Journal of neuroengineering and rehabilitation</Title><ISOAbbreviation>J Neuroeng Rehabil</ISOAbbreviation></Journal><ArticleTitle>Review of control strategies for robotic movement training after neurologic injury.</ArticleTitle><Pagination><MedlinePgn>20</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/1743-0003-6-20</ELocationID><Abstract><AbstractText>There is increasing interest in using robotic devices to assist in movement training following neurologic injuries such as stroke and spinal cord injury. This paper reviews control strategies for robotic therapy devices. Several categories of strategies have been proposed, including, assistive, challenge-based, haptic simulation, and coaching. The greatest amount of work has been done on developing assistive strategies, and thus the majority of this review summarizes techniques for implementing assistive strategies, including impedance-, counterbalance-, and EMG- based controllers, as well as adaptive controllers that modify control parameters based on ongoing participant performance. Clinical evidence regarding the relative effectiveness of different types of robotic therapy controllers is limited, but there is initial evidence that some control strategies are more effective than others. It is also now apparent there may be mechanisms by which some robotic control approaches might actually decrease the recovery possible with comparable, non-robotic forms of training. In future research, there is a need for head-to-head comparison of control algorithms in randomized, controlled clinical trials, and for improved models of human motor recovery to provide a more rational framework for designing robotic therapy control strategies.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Marchal-Crespo</LastName><ForeName>Laura</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA. lmarchal@uci.edu</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Reinkensmeyer</LastName><ForeName>David J</ForeName><Initials>DJ</Initials></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>M01RR00827</GrantID><Acronym>RR</Acronym><Agency>NCRR NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>N01-HD-3-3352</GrantID><Acronym>HD</Acronym><Agency>NICHD NIH 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Manipulations</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D012371">Robotics</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D020196">Trauma, Nervous System</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000534">rehabilitation</QualifierName></MeshHeading></MeshHeadingList><NumberOfReferences>174</NumberOfReferences><OtherID Source="NLM">PMC2710333</OtherID></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2008</Year><Month>10</Month><Day>18</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2009</Year><Month>6</Month><Day>16</Day></PubMedPubDate><PubMedPubDate PubStatus="aheadofprint"><Year>2009</Year><Month>6</Month><Day>16</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2009</Year><Month>6</Month><Day>18</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2009</Year><Month>6</Month><Day>18</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2009</Year><Month>8</Month><Day>28</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pii">1743-0003-6-20</ArticleId><ArticleId IdType="doi">10.1186/1743-0003-6-20</ArticleId><ArticleId IdType="pubmed">19531254</ArticleId><ArticleId IdType="pmc">PMC2710333</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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