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  <doc>
    <id>32606</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>181</pageFirst>
    <pageLast>182</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>image</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-01-24</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Machine Learning based Real Time Detection of Freezing of Gait of Parkinson Patients Running on a Body Worn Device</title>
    <abstract language="eng">For those who have Parkinson's disease, one of the most incapacitating symptoms is Freezing of Gait (FOG). Gait impairment and disruptions limit everyday activities and reduce quality of daily life along with the increase in the risk of falling [1]. Thanks to recent advancement in embedded electronics and sensors as well as their adaptation in the wearable device market, low power devices are becoming more and more capable running neural networks. This enables researchers to implement complex models on wearable devices that capture and analyze sensor data to detect FOGin real-time.</abstract>
    <parentTitle language="eng">IEEE/ACM international conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE 2023), 181 (2023)</parentTitle>
    <identifier type="isbn">979-8-4007-0102-3</identifier>
    <identifier type="issn">2832-2975</identifier>
    <identifier type="doi">10.1145/3580252.3589423</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="Publikationsweg">Open Access</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Ali</firstName>
      <lastName>Haddadi Esfahani</lastName>
    </author>
    <submitter>
      <firstName>Elisabeth</firstName>
      <lastName>Vogel</lastName>
    </submitter>
    <author>
      <firstName>Oliver</firstName>
      <lastName>Maye</lastName>
    </author>
    <author>
      <firstName>Max</firstName>
      <lastName>Frohberg</lastName>
    </author>
    <author>
      <firstName>Maria</firstName>
      <lastName>Speh</lastName>
    </author>
    <author>
      <firstName>Michael</firstName>
      <lastName>Jöbges</lastName>
    </author>
    <author>
      <firstName>Peter</firstName>
      <lastName>Langendörfer</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Parkinson's disease</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Wearable computers</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Neural networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Real-time systems</value>
    </subject>
    <collection role="institutes" number="1222">FG Systeme</collection>
  </doc>
</export-example>
