Journal article Open Access

Pressure Distribution Classification and Segmentation of Human Hands in Contact with the Robot Body

Albini Alessandro; Giorgio Cannata


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  <identifier identifierType="URL">https://zenodo.org/record/3706665</identifier>
  <creators>
    <creator>
      <creatorName>Albini Alessandro</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-1562-7044</nameIdentifier>
      <affiliation>University of Genoa</affiliation>
    </creator>
    <creator>
      <creatorName>Giorgio Cannata</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-7932-5411</nameIdentifier>
      <affiliation>University of Genoa</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Pressure Distribution Classification and Segmentation of Human Hands in Contact with the Robot Body</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>tactile sensing, robot skin, human robot interaction</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-03-10</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3706665</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1177/0278364920907688</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/collaborate_project</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;This paper deals with the problem of the recognition of human hand touch by a robot equipped with large area tactile&lt;br&gt;
sensors covering its body. This problem is relevant in the domain of physical human-robot interaction for discriminating&lt;br&gt;
between human and non-human contacts and to trigger and to drive cooperative tasks or robot motions, or to ensure a&lt;br&gt;
safe interaction. The underlying assumption, used in this paper, is that voluntary physical interaction tasks involve hand&lt;br&gt;
touch over the robot body, and therefore the capability of recognizing hand contacts is a key element to discriminate a&lt;br&gt;
purposive human touch from other types of interaction.&lt;br&gt;
The proposed approach is based on a geometric transformation of the tactile data, formed by pressure measurements&lt;br&gt;
associated to a non uniform cloud of 3D points (taxels) spread over a non linear manifold corresponding to the robot&lt;br&gt;
body, into tactile images representing the contact pressure distribution in 2D. Tactile images can be processed using&lt;br&gt;
deep learning algorithms to recognize human hands and to compute the pressure distribution applied by the various&lt;br&gt;
hand segments: palm and single fingers.&lt;br&gt;
Experimental results, performed on a real robot covered with robot skin, show the effectiveness of the proposed&lt;br&gt;
methodology. Moreover, to evaluate its robustness, various types of failures have been simulated. A further analysis&lt;br&gt;
concerning the transferability of the system has been performed, considering contacts occurring on a different&lt;br&gt;
sensorized robot part.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/820767/">820767</awardNumber>
      <awardTitle>Co-production CeLL performing Human-Robot Collaborative AssEmbly</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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