<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>22374</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>6</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>2018</volume>
    <type>articlenr</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-10-03</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Clustering of Human Gait with Parkinson's Disease by Using Dynamic Time Warping</title>
    <abstract language="eng">We present a new method for detecting gait disorders according to their stadium using cluster methods for sensor data. 21 healthy and 18 Parkinson subjects performed the Time Up and Go test. The time series were segmented into separate steps. For the analysis the horizontal acceleration measured by a mobile sensor system was considered. We used Dynamic Time Warping and Hierarchical Custering to distinguish the stadiums. A specificity of 92% was achieved.</abstract>
    <parentTitle language="eng">IEEE International Work Conference on Bioinspired Intelligence (IWOBI)</parentTitle>
    <identifier type="doi">10.1109/IWOBI.2018.8464203</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <author>
      <firstName>Tobias</firstName>
      <lastName>Steinmetzer</lastName>
    </author>
    <submitter>
      <firstName>Ingrid</firstName>
      <lastName>Bönninger</lastName>
    </submitter>
    <author>
      <firstName>Ingrid</firstName>
      <lastName>Bönninger</lastName>
    </author>
    <author>
      <firstName>Barbara</firstName>
      <lastName>Priwitzer</lastName>
    </author>
    <author>
      <firstName>Fritjof</firstName>
      <lastName>Reinhardt</lastName>
    </author>
    <author>
      <firstName>Markus Christoph</firstName>
      <lastName>Reckhardt</lastName>
    </author>
    <author>
      <firstName>Dorela</firstName>
      <lastName>Erk</lastName>
    </author>
    <author>
      <firstName>Carlos M.</firstName>
      <lastName>Travieso-González</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>DTW</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>clustering</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>parkinson disease</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>time series</value>
    </subject>
    <collection role="institutes" number="1400">Institut für Medizintechnologie</collection>
  </doc>
  <doc>
    <id>24422</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>17857</pageFirst>
    <pageLast>17868</pageLast>
    <pageNumber/>
    <edition/>
    <issue>24</issue>
    <volume>32(2020)</volume>
    <type>articlenr</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation>Niederlausitz Clinic, Center of Neurology and Pain Management Senftenberg Germany, University of Las Palmas de Gran Canaria, Signals and Communication Department, IDeTIC Las Palmas de Gran Canaria Spain</contributingCorporation>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2019-09-26</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Comparison of algorithms and classifiers for stride detection using wearables</title>
    <abstract language="eng">Sensor-based systems for diagnosis or therapy support of motor dysfunctions need methodologies of automatically stride detection from movement sequences. In this proposal, we developed a stride detection system for daily life use. We compared mostly used algorithms min–max patterns, dynamic time warping, convolutional neural networks (CNN), and automatic framing using two data sets of 32 healthy and 28 Parkinson’s disease (PD) persons. We developed an insole with force and IMU sensors to record the gait data. The PD patients carried out the standardized time up and go test, and the healthy persons a daily life activities test (walking, sitting, standing, ascending and descending stairs). As an automatically stride detection process for daily life use, we propose a first stride detection using automatic framing, and after normalization and resampling data a CNN is used. A F1-score of 0.938 (recall 0.968, precision 0.910) for time up and go test and of 0.944 (recall 0.992, precision 0.901) for daily life activities test were obtained for CNN. Compared to the other detection methods, up to 6% F-measure improvement was shown.</abstract>
    <parentTitle language="eng">Neural Computing and Applications</parentTitle>
    <identifier type="doi">10.1007/s00521-019-04384-6</identifier>
    <identifier type="issn">1433-3058</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <author>
      <firstName>Tobias</firstName>
      <lastName>Steinmetzer</lastName>
    </author>
    <submitter>
      <firstName>Ingrid</firstName>
      <lastName>Bönninger</lastName>
    </submitter>
    <author>
      <firstName>Markus Christoph</firstName>
      <lastName>Reckhardt</lastName>
    </author>
    <author>
      <firstName>Fritjof</firstName>
      <lastName>Reinhardt</lastName>
    </author>
    <author>
      <firstName>Dorela</firstName>
      <lastName>Erk</lastName>
    </author>
    <author>
      <firstName>Carlos M.</firstName>
      <lastName>Travieso-González</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Stride detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Gait analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Inertial sensors</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Parkinson’s disease</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Validation Dynamic time warping</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Time up and go test</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Convolutional neural networks</value>
    </subject>
    <collection role="institutes" number="1400">Institut für Medizintechnologie</collection>
  </doc>
</export-example>
