Journal article Open Access

Hand Motion Analysis using CNN

Harsh Raj; Aditya Duggal; Aditya Kumar Shetty M; Sreekanth Uppara; Srividya M S


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  <identifier identifierType="URL">https://zenodo.org/record/5533623</identifier>
  <creators>
    <creator>
      <creatorName>Harsh Raj</creatorName>
      <affiliation>Dept. of Computer Science and Engineering, R.V. College  of Engineering, Bengaluru, India.</affiliation>
    </creator>
    <creator>
      <creatorName>Aditya Duggal</creatorName>
      <affiliation>Dept. of Computer Science and Engineering, R.V. College  of Engineering, Bengaluru, India.</affiliation>
    </creator>
    <creator>
      <creatorName>Aditya Kumar Shetty M</creatorName>
      <affiliation>Dept. of Computer Science and Engineering, R.V. College  of Engineering, Bengaluru, India.</affiliation>
    </creator>
    <creator>
      <creatorName>Sreekanth Uppara</creatorName>
      <affiliation>Dept. of Computer Science and Engineering, R.V. College  of Engineering, Bengaluru, India.</affiliation>
    </creator>
    <creator>
      <creatorName>Srividya M S</creatorName>
      <affiliation>Dept. of Computer Science and Engineering, R.V. College  of Engineering, Bengaluru, India.</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Hand Motion Analysis using CNN</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>Convolutional Neural Network, Human Computer Interaction, Robust, Testing accuracy</subject>
    <subject subjectScheme="issn">2231-2307</subject>
    <subject subjectScheme="handle">F3409039620/2020©BEIESP</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-05-30</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5533623</alternateIdentifier>
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    <relatedIdentifier relatedIdentifierType="ISSN" relationType="IsCitedBy" resourceTypeGeneral="JournalArticle">2231-2307</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.35940/ijsce.F3409.059620</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;Hand motion detection and gesture recognition research has attracted large interest due to its wide range of applications in the field of Human computer interaction such as sign language recognition, 3D printing, virtual reality. There have been several approaches to create a robust algorithm to ease human computer interaction and perform in unfavourable environments.The real time recognition and learning of the model are big challenges. In this work, we use Convolutional Neural Network architecture to detect and classify hand motions, the region of interest of the image is passed through the neural network for the hand motion analysis and detection.Our system has achieved testing accuracy of 98%.&lt;/p&gt;</description>
  </descriptions>
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