Review of control strategies for robotic movement training after neurologic injury
Identifieur interne : 001169 ( Ncbi/Merge ); précédent : 001168; suivant : 001170Review of control strategies for robotic movement training after neurologic injury
Auteurs : Laura Marchal-Crespo [États-Unis] ; David J. Reinkensmeyer [États-Unis]Source :
- 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.
Url:
DOI: 10.1186/1743-0003-6-20
PubMed: 19531254
PubMed Central: 2710333
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PMC:2710333Le document en format XML
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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.</p></div></front><back><div1 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pmid="19531254"><pmc><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 wicri:level="1"><nlm:aff id="I1">Department of Mechanical and Aerospace Engineering, University of California, Irvine, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Mechanical and Aerospace Engineering, University of California, Irvine</wicri:regionArea><wicri:noRegion>Irvine</wicri:noRegion></affiliation></author><author><name sortKey="Reinkensmeyer, David J" sort="Reinkensmeyer, David J" uniqKey="Reinkensmeyer D" first="David J" last="Reinkensmeyer">David J. Reinkensmeyer</name><affiliation wicri:level="1"><nlm:aff id="I1">Department of Mechanical and Aerospace Engineering, University of California, Irvine, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Mechanical and Aerospace Engineering, University of California, Irvine</wicri:regionArea><wicri:noRegion>Irvine</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:aff id="I2">Department of Biomedical Engineering, University of California, Irvine, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biomedical Engineering, University of California, Irvine</wicri:regionArea><wicri:noRegion>Irvine</wicri:noRegion></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">19531254</idno><idno type="pmc">2710333</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2710333</idno><idno type="RBID">PMC:2710333</idno><idno type="doi">10.1186/1743-0003-6-20</idno><date when="2009">2009</date><idno type="wicri:Area/Pmc/Corpus">000B89</idno><idno type="wicri:Area/Pmc/Curation">000B89</idno><idno type="wicri:Area/Pmc/Checkpoint">002060</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">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 wicri:level="1"><nlm:aff id="I1">Department of Mechanical and Aerospace Engineering, University of California, Irvine, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Mechanical and Aerospace Engineering, University of California, Irvine</wicri:regionArea><wicri:noRegion>Irvine</wicri:noRegion></affiliation></author><author><name sortKey="Reinkensmeyer, David J" sort="Reinkensmeyer, David J" uniqKey="Reinkensmeyer D" first="David J" last="Reinkensmeyer">David J. Reinkensmeyer</name><affiliation wicri:level="1"><nlm:aff id="I1">Department of Mechanical and Aerospace Engineering, University of California, Irvine, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Mechanical and Aerospace Engineering, University of California, Irvine</wicri:regionArea><wicri:noRegion>Irvine</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:aff id="I2">Department of Biomedical Engineering, University of California, Irvine, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biomedical Engineering, University of California, Irvine</wicri:regionArea><wicri:noRegion>Irvine</wicri:noRegion></affiliation></author></analytic><series><title level="j">Journal of NeuroEngineering and Rehabilitation</title><idno type="eISSN">1743-0003</idno><imprint><date when="2009">2009</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>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.</p></div></front><back><div1 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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 wicri:level="2"><nlm:affiliation>Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA. lmarchal@uci.edu</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA</wicri:regionArea><placeName><region type="state">Californie</region></placeName></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><idno type="wicri:Area/PubMed/Curation">001260</idno><idno type="wicri:Area/PubMed/Checkpoint">001059</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 wicri:level="2"><nlm:affiliation>Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA. lmarchal@uci.edu</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA</wicri:regionArea><placeName><region type="state">Californie</region></placeName></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></double></record>Pour manipuler ce document sous Unix (Dilib)
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