Implementation of Human-Machine Synchronization Control for Active Rehabilitation Using an Inertia Sensor
Identifieur interne : 002393 ( Main/Merge ); précédent : 002392; suivant : 002394Implementation of Human-Machine Synchronization Control for Active Rehabilitation Using an Inertia Sensor
Auteurs : Zhibin Song ; Shuxiang Guo [République populaire de Chine] ; Nan Xiao ; Baofeng Gao ; Liwei ShiSource :
- Sensors (Basel, Switzerland) [ 1424-8220 ] ; 2012.
Abstract
According to neuro-rehabilitation practice, active training is effective for mild stroke patients, which means these patients are able to recovery effective when they perform the training to overcome certain resistance by themselves. Therefore, for rehabilitation devices without backdrivability, implementation of human-machine synchronization is important and a precondition to perform active training. In this paper, a method to implement this precondition is proposed and applied in a user’s performance of elbow flexions and extensions when he wore an upper limb exoskeleton rehabilitation device (ULERD), which is portable, wearable and non-backdrivable. In this method, an inertia sensor is adapted to detect the motion of the user’s forearm. In order to get a smooth value of the velocity of the user’s forearm, an adaptive weighted average filtering is applied. On the other hand, to obtain accurate tracking performance, a double close-loop control is proposed to realize real-time and stable tracking. Experiments have been conducted to prove that these methods are effective and feasible for active rehabilitation.
Url:
DOI: 10.3390/s121216046
PubMed: 23443366
PubMed Central: 3571770
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E-Mails:<email>guo@eng.kagawa-u.ac.jp</email> (S.G.);<email>xiao@eng.kagawa-u.ac.jp</email> (N.X.);<email>gaobaofeng@eng.kagawa-u.ac.jp</email> (B.G.);<email>shi@eng.kagawa-u.ac.jp</email> (L.S.)</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">23443366</idno><idno type="pmc">3571770</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3571770</idno><idno type="RBID">PMC:3571770</idno><idno type="doi">10.3390/s121216046</idno><date when="2012">2012</date><idno type="wicri:Area/Pmc/Corpus">002495</idno><idno type="wicri:Area/Pmc/Curation">002495</idno><idno type="wicri:Area/Pmc/Checkpoint">001715</idno><idno type="wicri:Area/Ncbi/Merge">002537</idno><idno type="wicri:Area/Ncbi/Curation">002537</idno><idno type="wicri:Area/Ncbi/Checkpoint">002537</idno><idno type="wicri:Area/Main/Merge">002393</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Implementation of Human-Machine Synchronization Control for Active Rehabilitation Using an Inertia Sensor</title><author><name sortKey="Song, Zhibin" sort="Song, Zhibin" uniqKey="Song Z" first="Zhibin" last="Song">Zhibin Song</name><affiliation><nlm:aff id="af1-sensors-12-16046">Department of Intelligent Mechanical Systems Engineering, Kagawa University, Hayashi-cho, Takamatsu 761-0369, Japan; E-Mails:<email>guo@eng.kagawa-u.ac.jp</email> (S.G.);<email>xiao@eng.kagawa-u.ac.jp</email> (N.X.);<email>gaobaofeng@eng.kagawa-u.ac.jp</email> (B.G.);<email>shi@eng.kagawa-u.ac.jp</email> (L.S.)</nlm:aff></affiliation></author><author><name sortKey="Guo, Shuxiang" sort="Guo, Shuxiang" uniqKey="Guo S" first="Shuxiang" last="Guo">Shuxiang Guo</name><affiliation><nlm:aff id="af1-sensors-12-16046">Department of Intelligent Mechanical Systems Engineering, Kagawa University, Hayashi-cho, Takamatsu 761-0369, Japan; E-Mails:<email>guo@eng.kagawa-u.ac.jp</email> (S.G.);<email>xiao@eng.kagawa-u.ac.jp</email> (N.X.);<email>gaobaofeng@eng.kagawa-u.ac.jp</email> (B.G.);<email>shi@eng.kagawa-u.ac.jp</email> (L.S.)</nlm:aff></affiliation><affiliation wicri:level="1"><nlm:aff id="af2-sensors-12-16046">Automation College, Harbin Engineering University, 145 Nantong Street, Harbin, Heilongjiang 150001, China</nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Automation College, Harbin Engineering University, 145 Nantong Street, Harbin, Heilongjiang 150001</wicri:regionArea><wicri:noRegion>Heilongjiang 150001</wicri:noRegion></affiliation></author><author><name sortKey="Xiao, Nan" sort="Xiao, Nan" uniqKey="Xiao N" first="Nan" last="Xiao">Nan Xiao</name><affiliation><nlm:aff id="af1-sensors-12-16046">Department of Intelligent Mechanical Systems Engineering, Kagawa University, Hayashi-cho, Takamatsu 761-0369, Japan; E-Mails:<email>guo@eng.kagawa-u.ac.jp</email> (S.G.);<email>xiao@eng.kagawa-u.ac.jp</email> (N.X.);<email>gaobaofeng@eng.kagawa-u.ac.jp</email> (B.G.);<email>shi@eng.kagawa-u.ac.jp</email> (L.S.)</nlm:aff></affiliation></author><author><name sortKey="Gao, Baofeng" sort="Gao, Baofeng" uniqKey="Gao B" first="Baofeng" last="Gao">Baofeng Gao</name><affiliation><nlm:aff id="af1-sensors-12-16046">Department of Intelligent Mechanical Systems Engineering, Kagawa University, Hayashi-cho, Takamatsu 761-0369, Japan; E-Mails:<email>guo@eng.kagawa-u.ac.jp</email> (S.G.);<email>xiao@eng.kagawa-u.ac.jp</email> (N.X.);<email>gaobaofeng@eng.kagawa-u.ac.jp</email> (B.G.);<email>shi@eng.kagawa-u.ac.jp</email> (L.S.)</nlm:aff></affiliation></author><author><name sortKey="Shi, Liwei" sort="Shi, Liwei" uniqKey="Shi L" first="Liwei" last="Shi">Liwei Shi</name><affiliation><nlm:aff id="af1-sensors-12-16046">Department of Intelligent Mechanical Systems Engineering, Kagawa University, Hayashi-cho, Takamatsu 761-0369, Japan; E-Mails:<email>guo@eng.kagawa-u.ac.jp</email> (S.G.);<email>xiao@eng.kagawa-u.ac.jp</email> (N.X.);<email>gaobaofeng@eng.kagawa-u.ac.jp</email> (B.G.);<email>shi@eng.kagawa-u.ac.jp</email> (L.S.)</nlm:aff></affiliation></author></analytic><series><title level="j">Sensors (Basel, Switzerland)</title><idno type="eISSN">1424-8220</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>According to neuro-rehabilitation practice, active training is effective for mild stroke patients, which means these patients are able to recovery effective when they perform the training to overcome certain resistance by themselves. Therefore, for rehabilitation devices without backdrivability, implementation of human-machine synchronization is important and a precondition to perform active training. In this paper, a method to implement this precondition is proposed and applied in a user’s performance of elbow flexions and extensions when he wore an upper limb exoskeleton rehabilitation device (ULERD), which is portable, wearable and non-backdrivable. In this method, an inertia sensor is adapted to detect the motion of the user’s forearm. In order to get a smooth value of the velocity of the user’s forearm, an adaptive weighted average filtering is applied. On the other hand, to obtain accurate tracking performance, a double close-loop control is proposed to realize real-time and stable tracking. Experiments have been conducted to prove that these methods are effective and feasible for active rehabilitation.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Goldstein, L" uniqKey="Goldstein L">L. Goldstein</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lindberg, P" uniqKey="Lindberg P">P. Lindberg</name></author><author><name sortKey="Schmitz, C" uniqKey="Schmitz C">C. Schmitz</name></author><author><name sortKey="Forssberg, H" uniqKey="Forssberg H">H. Forssberg</name></author><author><name sortKey="Engardt, M" uniqKey="Engardt M">M. Engardt</name></author><author><name sortKey="Borg, J" uniqKey="Borg J">J. Borg</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Tian, Y" uniqKey="Tian Y">Y. Tian</name></author><author><name sortKey="Shi, L" uniqKey="Shi L">L. Shi</name></author><author><name sortKey="Jing, L" uniqKey="Jing L">L. 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