Journal article Open Access

Surgical Hand Gesture Prediction for the Operating Room

Inna Skarga-Bandurova; Rostislav Siriak; Tetiana Biloborodova; Fabio Cuzzolin; Vivek Singh Bawa; Mohamed Ibrahim Mohamed; R Dinesh Jackson


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  <identifier identifierType="URL">https://zenodo.org/record/4471560</identifier>
  <creators>
    <creator>
      <creatorName>Inna Skarga-Bandurova</creatorName>
      <affiliation>OBU</affiliation>
    </creator>
    <creator>
      <creatorName>Rostislav Siriak</creatorName>
      <affiliation>OBU</affiliation>
    </creator>
    <creator>
      <creatorName>Tetiana Biloborodova</creatorName>
      <affiliation>OBU</affiliation>
    </creator>
    <creator>
      <creatorName>Fabio Cuzzolin</creatorName>
      <affiliation>OBU</affiliation>
    </creator>
    <creator>
      <creatorName>Vivek Singh Bawa</creatorName>
      <affiliation>OBU</affiliation>
    </creator>
    <creator>
      <creatorName>Mohamed Ibrahim Mohamed</creatorName>
      <affiliation>OBU</affiliation>
    </creator>
    <creator>
      <creatorName>R Dinesh Jackson</creatorName>
      <affiliation>OBU</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Surgical Hand Gesture Prediction for the Operating Room</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>surgical robot</subject>
    <subject>GestureConvLSTM</subject>
    <subject>ConvLSTM</subject>
    <subject>operating room</subject>
    <subject>prediction; surgeon</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-09-04</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4471560</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.3233/SHTI200621</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/saras-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;Technological advancements in smart assistive technology enable navigating and manipulating various types of computer-aided devices in the operating room through a contactless gesture interface. Understanding surgeon actions is crucial to natural human-robot interaction in operating room since it means a sort of prediction a human behavior so that the robot can foresee the surgeon&amp;#39;s intention, early choose appropriate action and reduce waiting time. In this paper, we present a new deep network based on Convolution Long Short-Term Memory (ConvLSTM) for gesture prediction configured to provide natural interaction between the surgeon and assistive robot and improve operating-room efficiency. The experimental results prove the capability of reliably recognizing unfinished gestures on videos. We quantitatively demonstrate the latter ability and the fact that GestureConvLSTM improves the baseline system performance on LSA64 dataset.&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/H2020/779813/">779813</awardNumber>
      <awardTitle>Smart Autonomous Robotic Assistant Surgeon</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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