Performance and Usability of Various Robotic Arm Control Modes from Human Force Signals.
Identifieur interne : 000329 ( PubMed/Curation ); précédent : 000328; suivant : 000330Performance and Usability of Various Robotic Arm Control Modes from Human Force Signals.
Auteurs : Sébastien Mick [France] ; Daniel Cattaert [France] ; Florent Paclet [France] ; Pierre-Yves Oudeyer [France] ; Aymar De Rugy [France]Source :
- Frontiers in neurorobotics [ 1662-5218 ] ; 2017.
Abstract
Elaborating an efficient and usable mapping between input commands and output movements is still a key challenge for the design of robotic arm prostheses. In order to address this issue, we present and compare three different control modes, by assessing them in terms of performance as well as general usability. Using an isometric force transducer as the command device, these modes convert the force input signal into either a position or a velocity vector, whose magnitude is linearly or quadratically related to force input magnitude. With the robotic arm from the open source 3D-printed Poppy Humanoid platform simulating a mobile prosthesis, an experiment was carried out with eighteen able-bodied subjects performing a 3-D target-reaching task using each of the three modes. The subjects were given questionnaires to evaluate the quality of their experience with each mode, providing an assessment of their global usability in the context of the task. According to performance metrics and questionnaire results, velocity control modes were found to perform better than position control mode in terms of accuracy and quality of control as well as user satisfaction and comfort. Subjects also seemed to favor quadratic velocity control over linear (proportional) velocity control, even if these two modes did not clearly distinguish from one another when it comes to performance and usability assessment. These results highlight the need to take into account user experience as one of the key criteria for the design of control modes intended to operate limb prostheses.
DOI: 10.3389/fnbot.2017.00055
PubMed: 29118699
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Bordeaux, Bordeaux</wicri:regionArea></affiliation></author><author><name sortKey="Oudeyer, Pierre Yves" sort="Oudeyer, Pierre Yves" uniqKey="Oudeyer P" first="Pierre-Yves" last="Oudeyer">Pierre-Yves Oudeyer</name><affiliation wicri:level="1"><nlm:affiliation>Flowers Team, INRIA Bordeaux Sud-Ouest, Talence, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>Flowers Team, INRIA Bordeaux Sud-Ouest, Talence</wicri:regionArea></affiliation></author><author><name sortKey="De Rugy, Aymar" sort="De Rugy, Aymar" uniqKey="De Rugy A" first="Aymar" last="De Rugy">Aymar De Rugy</name><affiliation wicri:level="1"><nlm:affiliation>Hybrid Team, Institut de Neurosciences Cognitives et Intégratives d'Aquitaine, CNRS UMR 5287, Univ. Bordeaux, Bordeaux, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>Hybrid Team, Institut de Neurosciences Cognitives et Intégratives d'Aquitaine, CNRS UMR 5287, Univ. Bordeaux, Bordeaux</wicri:regionArea></affiliation></author></analytic><series><title level="j">Frontiers in neurorobotics</title><idno type="ISSN">1662-5218</idno><imprint><date when="2017" type="published">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Elaborating an efficient and usable mapping between input commands and output movements is still a key challenge for the design of robotic arm prostheses. In order to address this issue, we present and compare three different control modes, by assessing them in terms of performance as well as general usability. Using an isometric force transducer as the command device, these modes convert the force input signal into either a position or a velocity vector, whose magnitude is linearly or quadratically related to force input magnitude. With the robotic arm from the open source 3D-printed Poppy Humanoid platform simulating a mobile prosthesis, an experiment was carried out with eighteen able-bodied subjects performing a 3-D target-reaching task using each of the three modes. The subjects were given questionnaires to evaluate the quality of their experience with each mode, providing an assessment of their global usability in the context of the task. According to performance metrics and questionnaire results, velocity control modes were found to perform better than position control mode in terms of accuracy and quality of control as well as user satisfaction and comfort. Subjects also seemed to favor quadratic velocity control over linear (proportional) velocity control, even if these two modes did not clearly distinguish from one another when it comes to performance and usability assessment. These results highlight the need to take into account user experience as one of the key criteria for the design of control modes intended to operate limb prostheses.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">29118699</PMID><DateCreated><Year>2017</Year><Month>11</Month><Day>09</Day></DateCreated><DateRevised><Year>2017</Year><Month>11</Month><Day>12</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Print">1662-5218</ISSN><JournalIssue CitedMedium="Print"><Volume>11</Volume><PubDate><Year>2017</Year></PubDate></JournalIssue><Title>Frontiers in neurorobotics</Title><ISOAbbreviation>Front Neurorobot</ISOAbbreviation></Journal><ArticleTitle>Performance and Usability of Various Robotic Arm Control Modes from Human Force Signals.</ArticleTitle><Pagination><MedlinePgn>55</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.3389/fnbot.2017.00055</ELocationID><Abstract><AbstractText>Elaborating an efficient and usable mapping between input commands and output movements is still a key challenge for the design of robotic arm prostheses. In order to address this issue, we present and compare three different control modes, by assessing them in terms of performance as well as general usability. Using an isometric force transducer as the command device, these modes convert the force input signal into either a position or a velocity vector, whose magnitude is linearly or quadratically related to force input magnitude. With the robotic arm from the open source 3D-printed Poppy Humanoid platform simulating a mobile prosthesis, an experiment was carried out with eighteen able-bodied subjects performing a 3-D target-reaching task using each of the three modes. The subjects were given questionnaires to evaluate the quality of their experience with each mode, providing an assessment of their global usability in the context of the task. According to performance metrics and questionnaire results, velocity control modes were found to perform better than position control mode in terms of accuracy and quality of control as well as user satisfaction and comfort. Subjects also seemed to favor quadratic velocity control over linear (proportional) velocity control, even if these two modes did not clearly distinguish from one another when it comes to performance and usability assessment. These results highlight the need to take into account user experience as one of the key criteria for the design of control modes intended to operate limb prostheses.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Mick</LastName><ForeName>Sébastien</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Flowers Team, INRIA Bordeaux Sud-Ouest, Talence, France.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Hybrid Team, Institut de Neurosciences Cognitives et Intégratives d'Aquitaine, CNRS UMR 5287, Univ. Bordeaux, Bordeaux, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Cattaert</LastName><ForeName>Daniel</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Hybrid Team, Institut de Neurosciences Cognitives et Intégratives d'Aquitaine, CNRS UMR 5287, Univ. Bordeaux, Bordeaux, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Paclet</LastName><ForeName>Florent</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>Hybrid Team, Institut de Neurosciences Cognitives et Intégratives d'Aquitaine, CNRS UMR 5287, Univ. Bordeaux, Bordeaux, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Oudeyer</LastName><ForeName>Pierre-Yves</ForeName><Initials>PY</Initials><AffiliationInfo><Affiliation>Flowers Team, INRIA Bordeaux Sud-Ouest, Talence, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>de Rugy</LastName><ForeName>Aymar</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Hybrid Team, Institut de Neurosciences Cognitives et Intégratives d'Aquitaine, CNRS UMR 5287, Univ. Bordeaux, Bordeaux, France.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Centre for Sensorimotor Performance, School of Human Movement and Nutrition Sciences, University of Queensland, Brisbane, QLD, Australia.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2017</Year><Month>10</Month><Day>25</Day></ArticleDate></Article><MedlineJournalInfo><Country>Switzerland</Country><MedlineTA>Front Neurorobot</MedlineTA><NlmUniqueID>101477958</NlmUniqueID><ISSNLinking>1662-5218</ISSNLinking></MedlineJournalInfo><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>J Mot Behav. 1988 Mar;20(1):53-66</RefSource><PMID Version="1">15075132</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Neurophysiol. 2012 Dec;108(12):3333-41</RefSource><PMID Version="1">23019006</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Neural Eng. 2008 Dec;5(4):455-76</RefSource><PMID Version="1">19015583</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Neural Eng. 2012 Apr;9(2):026027</RefSource><PMID Version="1">22427488</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Neurosci. 2012 May 23;32(21):7384-91</RefSource><PMID Version="1">22623684</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Prosthet Orthot Int. 2007 Sep;31(3):236-57</RefSource><PMID Version="1">17979010</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Rehabil Res Dev. 2011;48(6):697-706</RefSource><PMID Version="1">21938656</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>IEEE Trans Biomed Eng. 2010 Oct;57(10):2495-505</RefSource><PMID Version="1">20615806</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Front Neuroinform. 2009 Jan 15;2:10</RefSource><PMID Version="1">19198666</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>IEEE Trans Neural Syst Rehabil Eng. 2014 Jul;22(4):797-809</RefSource><PMID Version="1">24760934</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Nat Neurosci. 2012 Dec;15(12):1752-7</RefSource><PMID Version="1">23160043</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Neurophysiol. 2014 Jul 15;112(2):411-29</RefSource><PMID Version="1">24717350</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Clin Rehabil. 1999 Aug;13(4):288-94</RefSource><PMID Version="1">10460116</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Neurosci. 2013 Jul 24;33(30):12384-94</RefSource><PMID Version="1">23884944</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Comput Biol Med. 2002 Jan;32(1):25-36</RefSource><PMID Version="1">11738638</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>IEEE Trans Neural Syst Rehabil Eng. 2015 Jul;23(4):618-27</RefSource><PMID Version="1">25680209</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Neuroeng Rehabil. 2015 Jun 13;12:53</RefSource><PMID Version="1">26071402</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Clin Ther. 2007 Apr;29(4):650-60</RefSource><PMID Version="1">17617288</PMID></CommentsCorrections></CommentsCorrectionsList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">arm prosthesis</Keyword><Keyword MajorTopicYN="N">control mode</Keyword><Keyword MajorTopicYN="N">neuroprosthesis</Keyword><Keyword MajorTopicYN="N">real-time control</Keyword><Keyword MajorTopicYN="N">robotic arm</Keyword><Keyword MajorTopicYN="N">usability testing</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2017</Year><Month>05</Month><Day>18</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2017</Year><Month>09</Month><Day>27</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2017</Year><Month>11</Month><Day>10</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2017</Year><Month>11</Month><Day>10</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2017</Year><Month>11</Month><Day>10</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">29118699</ArticleId><ArticleId IdType="doi">10.3389/fnbot.2017.00055</ArticleId><ArticleId IdType="pmc">PMC5660981</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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