Recognition of haptic interaction patterns in dyadic joint object manipulation.
Identifieur interne : 000463 ( PubMed/Corpus ); précédent : 000462; suivant : 000464Recognition of haptic interaction patterns in dyadic joint object manipulation.
Auteurs : Cigil Ece Madan ; Ayse Kucukyilmaz ; Tevfik Metin Sezgin ; Cagatay BasdoganSource :
- IEEE transactions on haptics [ 2329-4051 ]
English descriptors
- KwdEn :
- MESH :
- methods : Pattern Recognition, Automated.
- physiology : Touch.
- Cooperative Behavior, Humans, Man-Machine Systems, Models, Biological, Support Vector Machine, Task Performance and Analysis.
Abstract
The development of robots that can physically cooperate with humans has attained interest in the last decades. Obviously, this effort requires a deep understanding of the intrinsic properties of interaction. Up to now, many researchers have focused on inferring human intents in terms of intermediate or terminal goals in physical tasks. On the other hand, working side by side with people, an autonomous robot additionally needs to come up with in-depth information about underlying haptic interaction patterns that are typically encountered during human-human cooperation. However, to our knowledge, no study has yet focused on characterizing such detailed information. In this sense, this work is pioneering as an effort to gain deeper understanding of interaction patterns involving two or more humans in a physical task. We present a labeled human-human-interaction dataset, which captures the interaction of two humans, who collaboratively transport an object in an haptics-enabled virtual environment. In the light of information gained by studying this dataset, we propose that the actions of cooperating partners can be examined under three interaction types: In any cooperative task, the interacting humans either 1) work in harmony, 2) cope with conflicts, or 3) remain passive during interaction. In line with this conception, we present a taxonomy of human interaction patterns; then propose five different feature sets, comprising force-, velocity-and power-related information, for the classification of these patterns. Our evaluation shows that using a multi-class support vector machine (SVM) classifier, we can accomplish a correct classification rate of 86 percent for the identification of interaction patterns, an accuracy obtained by fusing a selected set of most informative features by Minimum Redundancy Maximum Relevance (mRMR) feature selection method.
DOI: 10.1109/TOH.2014.2384049
PubMed: 25532210
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Obviously, this effort requires a deep understanding of the intrinsic properties of interaction. Up to now, many researchers have focused on inferring human intents in terms of intermediate or terminal goals in physical tasks. On the other hand, working side by side with people, an autonomous robot additionally needs to come up with in-depth information about underlying haptic interaction patterns that are typically encountered during human-human cooperation. However, to our knowledge, no study has yet focused on characterizing such detailed information. In this sense, this work is pioneering as an effort to gain deeper understanding of interaction patterns involving two or more humans in a physical task. We present a labeled human-human-interaction dataset, which captures the interaction of two humans, who collaboratively transport an object in an haptics-enabled virtual environment. In the light of information gained by studying this dataset, we propose that the actions of cooperating partners can be examined under three interaction types: In any cooperative task, the interacting humans either 1) work in harmony, 2) cope with conflicts, or 3) remain passive during interaction. In line with this conception, we present a taxonomy of human interaction patterns; then propose five different feature sets, comprising force-, velocity-and power-related information, for the classification of these patterns. Our evaluation shows that using a multi-class support vector machine (SVM) classifier, we can accomplish a correct classification rate of 86 percent for the identification of interaction patterns, an accuracy obtained by fusing a selected set of most informative features by Minimum Redundancy Maximum Relevance (mRMR) feature selection method.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">25532210</PMID><DateCreated><Year>2015</Year><Month>03</Month><Day>21</Day></DateCreated><DateCompleted><Year>2015</Year><Month>11</Month><Day>16</Day></DateCompleted><DateRevised><Year>2015</Year><Month>11</Month><Day>19</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">2329-4051</ISSN><JournalIssue CitedMedium="Internet"><Volume>8</Volume><Issue>1</Issue><PubDate><MedlineDate>2015 Jan-Mar</MedlineDate></PubDate></JournalIssue><Title>IEEE transactions on haptics</Title><ISOAbbreviation>IEEE Trans Haptics</ISOAbbreviation></Journal><ArticleTitle>Recognition of haptic interaction patterns in dyadic joint object manipulation.</ArticleTitle><Pagination><MedlinePgn>54-66</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1109/TOH.2014.2384049</ELocationID><Abstract><AbstractText>The development of robots that can physically cooperate with humans has attained interest in the last decades. Obviously, this effort requires a deep understanding of the intrinsic properties of interaction. Up to now, many researchers have focused on inferring human intents in terms of intermediate or terminal goals in physical tasks. On the other hand, working side by side with people, an autonomous robot additionally needs to come up with in-depth information about underlying haptic interaction patterns that are typically encountered during human-human cooperation. However, to our knowledge, no study has yet focused on characterizing such detailed information. In this sense, this work is pioneering as an effort to gain deeper understanding of interaction patterns involving two or more humans in a physical task. We present a labeled human-human-interaction dataset, which captures the interaction of two humans, who collaboratively transport an object in an haptics-enabled virtual environment. In the light of information gained by studying this dataset, we propose that the actions of cooperating partners can be examined under three interaction types: In any cooperative task, the interacting humans either 1) work in harmony, 2) cope with conflicts, or 3) remain passive during interaction. In line with this conception, we present a taxonomy of human interaction patterns; then propose five different feature sets, comprising force-, velocity-and power-related information, for the classification of these patterns. Our evaluation shows that using a multi-class support vector machine (SVM) classifier, we can accomplish a correct classification rate of 86 percent for the identification of interaction patterns, an accuracy obtained by fusing a selected set of most informative features by Minimum Redundancy Maximum Relevance (mRMR) feature selection method.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Madan</LastName><ForeName>Cigil Ece</ForeName><Initials>CE</Initials></Author><Author ValidYN="Y"><LastName>Kucukyilmaz</LastName><ForeName>Ayse</ForeName><Initials>A</Initials></Author><Author ValidYN="Y"><LastName>Sezgin</LastName><ForeName>Tevfik Metin</ForeName><Initials>TM</Initials></Author><Author ValidYN="Y"><LastName>Basdogan</LastName><ForeName>Cagatay</ForeName><Initials>C</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2014</Year><Month>12</Month><Day>18</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>IEEE Trans Haptics</MedlineTA><NlmUniqueID>101491191</NlmUniqueID><ISSNLinking>1939-1412</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D003299">Cooperative Behavior</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D006801">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D008328">Man-Machine Systems</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D008954">Models, Biological</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D010363">Pattern Recognition, Automated</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D060388">Support Vector Machine</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D013647">Task Performance and Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D014110">Touch</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000502">physiology</QualifierName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="aheadofprint"><Year>2014</Year><Month>12</Month><Day>18</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2014</Year><Month>12</Month><Day>23</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2014</Year><Month>12</Month><Day>23</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2015</Year><Month>11</Month><Day>17</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="doi">10.1109/TOH.2014.2384049</ArticleId><ArticleId IdType="pubmed">25532210</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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