A computational framework for constructing interactive feedback for assisting motor learning.
Identifieur interne : 001D74 ( Ncbi/Merge ); précédent : 001D73; suivant : 001D75A computational framework for constructing interactive feedback for assisting motor learning.
Auteurs : Hari Sundaram [États-Unis] ; Yinpeng Chen ; Thanassis RikakisSource :
- Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference [ 1557-170X ] ; 2011.
English descriptors
- KwdEn :
- Biofeedback, Psychology (methods), Diagnosis, Computer-Assisted (methods), Humans, Learning, Movement, Paresis (physiopathology), Paresis (rehabilitation), Reproducibility of Results, Sensitivity and Specificity, Task Performance and Analysis, Therapy, Computer-Assisted (methods), User-Computer Interface.
- MESH :
- methods : Biofeedback, Psychology, Diagnosis, Computer-Assisted, Therapy, Computer-Assisted.
- physiopathology : Paresis.
- rehabilitation : Paresis.
- Humans, Learning, Movement, Reproducibility of Results, Sensitivity and Specificity, Task Performance and Analysis, User-Computer Interface.
Abstract
New motion capture technologies are allowing detailed, precise and complete monitoring of movement through real-time kinematic analysis. However, a clinically relevant understanding of movement impairment through kinematic analysis requires the development of computational models that integrate clinical expertise in the weighing of the kinematic parameters. The resulting kinematics based measures of movement impairment would further need to be integrated with existing clinical measures of activity disability. This is a challenging process requiring computational solutions that can extract correlations within and between three diverse data sets: human driven assessment of body function, kinematic based assessment of movement impairment and human driven assessment of activity. We propose to identify and characterize different sensorimotor control strategies used by normal individuals and by hemiparetic stroke survivors acquiring a skilled motor task. We will use novel quantitative approaches to further our understanding of how human motor function is coupled to multiple and simultaneous modes of feedback. The experiments rely on a novel interactive tasks environment developed by our team in which subjects are provided with rich auditory and visual feedback of movement variables to drive motor learning. Our proposed research will result in a computational framework for applying virtual information to assist motor learning for complex tasks that require coupling of proprioception, vision audio and haptic cues. We shall use the framework to devise a computational tool to assist with therapy of stroke survivors. This tool will utilize extracted relationships in a pre-clinical setting to generate effective and customized rehabilitation strategies.
DOI: 10.1109/IEMBS.2011.6090329
PubMed: 22254579
Links toward previous steps (curation, corpus...)
- to stream PubMed, to step Corpus: 000D21
- to stream PubMed, to step Curation: 000D21
- to stream PubMed, to step Checkpoint: 000E39
Links to Exploration step
pubmed:22254579Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">A computational framework for constructing interactive feedback for assisting motor learning.</title><author><name sortKey="Sundaram, Hari" sort="Sundaram, Hari" uniqKey="Sundaram H" first="Hari" last="Sundaram">Hari Sundaram</name><affiliation wicri:level="2"><nlm:affiliation>School of Arts Media and Engineering, Arizona State University, Tempe, AZ 85281, USA. hari.sundaram@asu.edu</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>School of Arts Media and Engineering, Arizona State University, Tempe, AZ 85281</wicri:regionArea><placeName><region type="state">Arizona</region></placeName></affiliation></author><author><name sortKey="Chen, Yinpeng" sort="Chen, Yinpeng" uniqKey="Chen Y" first="Yinpeng" last="Chen">Yinpeng Chen</name></author><author><name sortKey="Rikakis, Thanassis" sort="Rikakis, Thanassis" uniqKey="Rikakis T" first="Thanassis" last="Rikakis">Thanassis Rikakis</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2011">2011</date><idno type="doi">10.1109/IEMBS.2011.6090329</idno><idno type="RBID">pubmed:22254579</idno><idno type="pmid">22254579</idno><idno type="wicri:Area/PubMed/Corpus">000D21</idno><idno type="wicri:Area/PubMed/Curation">000D21</idno><idno type="wicri:Area/PubMed/Checkpoint">000E39</idno><idno type="wicri:Area/Ncbi/Merge">001D74</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">A computational framework for constructing interactive feedback for assisting motor learning.</title><author><name sortKey="Sundaram, Hari" sort="Sundaram, Hari" uniqKey="Sundaram H" first="Hari" last="Sundaram">Hari Sundaram</name><affiliation wicri:level="2"><nlm:affiliation>School of Arts Media and Engineering, Arizona State University, Tempe, AZ 85281, USA. hari.sundaram@asu.edu</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>School of Arts Media and Engineering, Arizona State University, Tempe, AZ 85281</wicri:regionArea><placeName><region type="state">Arizona</region></placeName></affiliation></author><author><name sortKey="Chen, Yinpeng" sort="Chen, Yinpeng" uniqKey="Chen Y" first="Yinpeng" last="Chen">Yinpeng Chen</name></author><author><name sortKey="Rikakis, Thanassis" sort="Rikakis, Thanassis" uniqKey="Rikakis T" first="Thanassis" last="Rikakis">Thanassis Rikakis</name></author></analytic><series><title level="j">Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference</title><idno type="ISSN">1557-170X</idno><imprint><date when="2011" type="published">2011</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Biofeedback, Psychology (methods)</term><term>Diagnosis, Computer-Assisted (methods)</term><term>Humans</term><term>Learning</term><term>Movement</term><term>Paresis (physiopathology)</term><term>Paresis (rehabilitation)</term><term>Reproducibility of Results</term><term>Sensitivity and Specificity</term><term>Task Performance and Analysis</term><term>Therapy, Computer-Assisted (methods)</term><term>User-Computer Interface</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Biofeedback, Psychology</term><term>Diagnosis, Computer-Assisted</term><term>Therapy, Computer-Assisted</term></keywords><keywords scheme="MESH" qualifier="physiopathology" xml:lang="en"><term>Paresis</term></keywords><keywords scheme="MESH" qualifier="rehabilitation" xml:lang="en"><term>Paresis</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Humans</term><term>Learning</term><term>Movement</term><term>Reproducibility of Results</term><term>Sensitivity and Specificity</term><term>Task Performance and Analysis</term><term>User-Computer Interface</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">New motion capture technologies are allowing detailed, precise and complete monitoring of movement through real-time kinematic analysis. However, a clinically relevant understanding of movement impairment through kinematic analysis requires the development of computational models that integrate clinical expertise in the weighing of the kinematic parameters. The resulting kinematics based measures of movement impairment would further need to be integrated with existing clinical measures of activity disability. This is a challenging process requiring computational solutions that can extract correlations within and between three diverse data sets: human driven assessment of body function, kinematic based assessment of movement impairment and human driven assessment of activity. We propose to identify and characterize different sensorimotor control strategies used by normal individuals and by hemiparetic stroke survivors acquiring a skilled motor task. We will use novel quantitative approaches to further our understanding of how human motor function is coupled to multiple and simultaneous modes of feedback. The experiments rely on a novel interactive tasks environment developed by our team in which subjects are provided with rich auditory and visual feedback of movement variables to drive motor learning. Our proposed research will result in a computational framework for applying virtual information to assist motor learning for complex tasks that require coupling of proprioception, vision audio and haptic cues. We shall use the framework to devise a computational tool to assist with therapy of stroke survivors. This tool will utilize extracted relationships in a pre-clinical setting to generate effective and customized rehabilitation strategies.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">22254579</PMID><DateCreated><Year>2012</Year><Month>01</Month><Day>18</Day></DateCreated><DateCompleted><Year>2012</Year><Month>06</Month><Day>12</Day></DateCompleted><DateRevised><Year>2014</Year><Month>08</Month><Day>21</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Print">1557-170X</ISSN><JournalIssue CitedMedium="Internet"><Volume>2011</Volume><PubDate><Year>2011</Year></PubDate></JournalIssue><Title>Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference</Title><ISOAbbreviation>Conf Proc IEEE Eng Med Biol Soc</ISOAbbreviation></Journal><ArticleTitle>A computational framework for constructing interactive feedback for assisting motor learning.</ArticleTitle><Pagination><MedlinePgn>1399-402</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1109/IEMBS.2011.6090329</ELocationID><Abstract><AbstractText>New motion capture technologies are allowing detailed, precise and complete monitoring of movement through real-time kinematic analysis. However, a clinically relevant understanding of movement impairment through kinematic analysis requires the development of computational models that integrate clinical expertise in the weighing of the kinematic parameters. The resulting kinematics based measures of movement impairment would further need to be integrated with existing clinical measures of activity disability. This is a challenging process requiring computational solutions that can extract correlations within and between three diverse data sets: human driven assessment of body function, kinematic based assessment of movement impairment and human driven assessment of activity. We propose to identify and characterize different sensorimotor control strategies used by normal individuals and by hemiparetic stroke survivors acquiring a skilled motor task. We will use novel quantitative approaches to further our understanding of how human motor function is coupled to multiple and simultaneous modes of feedback. The experiments rely on a novel interactive tasks environment developed by our team in which subjects are provided with rich auditory and visual feedback of movement variables to drive motor learning. Our proposed research will result in a computational framework for applying virtual information to assist motor learning for complex tasks that require coupling of proprioception, vision audio and haptic cues. We shall use the framework to devise a computational tool to assist with therapy of stroke survivors. This tool will utilize extracted relationships in a pre-clinical setting to generate effective and customized rehabilitation strategies.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Sundaram</LastName><ForeName>Hari</ForeName><Initials>H</Initials><AffiliationInfo><Affiliation>School of Arts Media and Engineering, Arizona State University, Tempe, AZ 85281, USA. hari.sundaram@asu.edu</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Chen</LastName><ForeName>Yinpeng</ForeName><Initials>Y</Initials></Author><Author ValidYN="Y"><LastName>Rikakis</LastName><ForeName>Thanassis</ForeName><Initials>T</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Conf Proc IEEE Eng Med Biol Soc</MedlineTA><NlmUniqueID>101243413</NlmUniqueID><ISSNLinking>1557-170X</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName MajorTopicYN="N" UI="D001676">Biofeedback, Psychology</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D003936">Diagnosis, Computer-Assisted</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D006801">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D007858">Learning</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D009068">Movement</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D010291">Paresis</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000503">physiopathology</QualifierName><QualifierName MajorTopicYN="Y" UI="Q000534">rehabilitation</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D015203">Reproducibility of Results</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D012680">Sensitivity and Specificity</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D013647">Task Performance and Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D013813">Therapy, Computer-Assisted</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D014584">User-Computer Interface</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2012</Year><Month>1</Month><Day>19</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2012</Year><Month>1</Month><Day>19</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2012</Year><Month>6</Month><Day>13</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="doi">10.1109/IEMBS.2011.6090329</ArticleId><ArticleId IdType="pubmed">22254579</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>États-Unis</li></country><region><li>Arizona</li></region></list><tree><noCountry><name sortKey="Chen, Yinpeng" sort="Chen, Yinpeng" uniqKey="Chen Y" first="Yinpeng" last="Chen">Yinpeng Chen</name><name sortKey="Rikakis, Thanassis" sort="Rikakis, Thanassis" uniqKey="Rikakis T" first="Thanassis" last="Rikakis">Thanassis Rikakis</name></noCountry><country name="États-Unis"><region name="Arizona"><name sortKey="Sundaram, Hari" sort="Sundaram, Hari" uniqKey="Sundaram H" first="Hari" last="Sundaram">Hari Sundaram</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Ticri/CIDE/explor/HapticV1/Data/Ncbi/Merge
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 001D74 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Ncbi/Merge/biblio.hfd -nk 001D74 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Ticri/CIDE
|area= HapticV1
|flux= Ncbi
|étape= Merge
|type= RBID
|clé= pubmed:22254579
|texte= A computational framework for constructing interactive feedback for assisting motor learning.
}}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/Ncbi/Merge/RBID.i -Sk "pubmed:22254579" \
| HfdSelect -Kh $EXPLOR_AREA/Data/Ncbi/Merge/biblio.hfd \
| NlmPubMed2Wicri -a HapticV1
| This area was generated with Dilib version V0.6.23. |  |