<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>17025</id>
    <completedYear/>
    <publishedYear>2013</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>5</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway, NJ</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2016-08-25</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Writer identification on mobile device based on handwritten</title>
    <parentTitle language="eng">47th International Carnahan Conference on Security Technology (ICCST), 2013, 8 - 11 Oct. 2013, Medellin</parentTitle>
    <identifier type="doi">10.1109/CCST.2013.6922063</identifier>
    <identifier type="isbn">978-1-4799-0889-9</identifier>
    <enrichment key="BTU">nicht an der BTU erstellt / not created at BTU</enrichment>
    <author>
      <firstName>Tobias</firstName>
      <lastName>Kutzner</lastName>
    </author>
    <submitter>
      <firstName>Tobias</firstName>
      <lastName>Kutzner</lastName>
    </submitter>
    <author>
      <firstName>Carlos M.</firstName>
      <lastName>Travieso-González</lastName>
    </author>
    <author>
      <firstName>Ingrid</firstName>
      <lastName>Bönninger</lastName>
    </author>
    <author>
      <firstName>Jesús B.</firstName>
      <lastName>Alonso</lastName>
    </author>
    <author>
      <firstName>Jose Luis</firstName>
      <lastName>Vásquez</lastName>
    </author>
    <collection role="institutes" number="1404">FG Softwareengineering</collection>
  </doc>
  <doc>
    <id>17309</id>
    <completedYear/>
    <publishedYear>2016</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>650</pageFirst>
    <pageLast>655</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway, NJ</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2016-09-28</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Online Handwriting Verification with Safe Password and Increasing Number of Features</title>
    <parentTitle language="eng">2016 Signal Processing and Integrated Networks (SPIN), 11-12 Feb. 2016</parentTitle>
    <identifier type="doi">10.1109/SPIN.2016.7566777</identifier>
    <identifier type="isbn">978-1-4673-9197-9</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <author>
      <firstName>Tobias</firstName>
      <lastName>Kutzner</lastName>
    </author>
    <submitter>
      <firstName>Tobias</firstName>
      <lastName>Kutzner</lastName>
    </submitter>
    <author>
      <firstName>Mario</firstName>
      <lastName>Dietze</lastName>
    </author>
    <author>
      <firstName>Ingrid</firstName>
      <lastName>Bönninger</lastName>
    </author>
    <author>
      <firstName>Carlos M.</firstName>
      <lastName>Travieso-González</lastName>
    </author>
    <author>
      <firstName>Anushikha</firstName>
      <lastName>Singh</lastName>
    </author>
    <author>
      <firstName>Malay Kishore</firstName>
      <lastName>Dutta</lastName>
    </author>
    <collection role="institutes" number="1404">FG Softwareengineering</collection>
  </doc>
  <doc>
    <id>17550</id>
    <completedYear/>
    <publishedYear>2014</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>63</pageFirst>
    <pageLast>71</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>WSEAS Press</publisherName>
    <publisherPlace>Athens [u.a.]</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2016-11-09</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Gait-based recognition of humans using Kinect camera</title>
    <parentTitle language="eng">Recent Advances in Computer Engineering, Communications and Information Technology, 8th International Conference on Communications and Information Technology (CIT '14), Tenerife, 2014</parentTitle>
    <identifier type="isbn">978-960-474-361-2</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <author>
      <firstName>Miguel</firstName>
      <lastName>Machado-Molina</lastName>
    </author>
    <submitter>
      <firstName>Ingrid</firstName>
      <lastName>Bönninger</lastName>
    </submitter>
    <author>
      <firstName>Ingrid</firstName>
      <lastName>Bönninger</lastName>
    </author>
    <author>
      <firstName>Malay Kishore</firstName>
      <lastName>Dutta</lastName>
    </author>
    <author>
      <firstName>Tobias</firstName>
      <lastName>Kutzner</lastName>
    </author>
    <author>
      <firstName>Carlos M.</firstName>
      <lastName>Travieso-González</lastName>
    </author>
    <collection role="institutes" number="1404">FG Softwareengineering</collection>
  </doc>
  <doc>
    <id>17554</id>
    <completedYear/>
    <publishedYear>2012</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2016-11-09</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Nutzer-Authentifizierung mittels handschriftlichen Passworten auf Mobil-Geräten</title>
    <abstract language="deu">Ziel des Beitrages ist es, das Potential von handschriftlichen Passworten für die Erhö- hung der Sicherheit beim Zugang zu Systemen zu untersuchen. Wir schlagen eine Mo- bilgerät-Server-Lösung mit verteilter Vorverarbeitung vor.&#13;
Untersucht werden die Erfolgsraten der korrekten Schreibererkennung bei der Verwen- dung nur eines Wortes, des Passwortes, der Reduktion der Anzahl der Passworte, die zum Trainieren des Klassifikationsalgorithmus verwendet werden, und der Reduktion der Parameter. Der implementierte Prototyp führt die Segmentierung auf dem Mobilgerät aus. Merkmalsextraktion und Klassifikation laufen als Server-Applikationen. Das Tool WEKA wird für die Ermittlung des geeignetsten Algorithmus für die Erkennung des Schreibers des Passwortes verwendet. Die Analysen werden mit 280 handschriftlichen Passworten durchgeführt. Davon sind 176 Originale und 104 Fälschungen. Als Mobilge- räte wurden ein HTC Desire mit Android 2.2 und ein Samsung Galaxy Ace mit Android 2.3.3 verwendet. Das vorgeschlagene System erkennt mit der Wahrscheinlichkeit von 96,87% den Schreiber des Passwortes, die False Acceptance Rate beträgt 12,5%, wenn acht Passworte jedes Schreibers für das Klassifikationsmodell verwendet werden.</abstract>
    <parentTitle language="deu">12. Wissenschaftstage der Hochschule Lausitz (FH) – University of Applied Sciences, 2012</parentTitle>
    <identifier type="url">http://www-docs.b-tu.de/forschung/public/wissenschaftstage/2012/informatik/Nutzerauthentifizierung_TKutzner.pdf</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <author>
      <firstName>Tobias</firstName>
      <lastName>Kutzner</lastName>
    </author>
    <submitter>
      <firstName>Ingrid</firstName>
      <lastName>Bönninger</lastName>
    </submitter>
    <author>
      <firstName>Ingrid</firstName>
      <lastName>Bönninger</lastName>
    </author>
    <author>
      <firstName>Carlos M.</firstName>
      <lastName>Travieso-González</lastName>
    </author>
    <collection role="institutes" number="1404">FG Softwareengineering</collection>
  </doc>
  <doc>
    <id>19571</id>
    <completedYear/>
    <publishedYear>2016</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>85</pageFirst>
    <pageLast>89</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway, NJ</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2017-03-20</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Study of Long-Term Quality of Online Signature Verification Systems</title>
    <parentTitle language="eng">IEEE 2nd International Conference on Communication, Control and Intelligent Systems (CCIS), 18-20 Nov. 2016</parentTitle>
    <identifier type="doi">10.1109/CCIntelS.2016.7878206</identifier>
    <identifier type="isbn">978-1-5090-3210-5</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <author>
      <firstName>Tobias</firstName>
      <lastName>Kutzner</lastName>
    </author>
    <submitter>
      <firstName>Tobias</firstName>
      <lastName>Kutzner</lastName>
    </submitter>
    <author>
      <firstName>Ingrid</firstName>
      <lastName>Bönninger</lastName>
    </author>
    <author>
      <firstName>Carlos M.</firstName>
      <lastName>Travieso-González</lastName>
    </author>
    <author>
      <firstName>Malay Kishore</firstName>
      <lastName>Dutta</lastName>
    </author>
    <author>
      <firstName>Anushikha</firstName>
      <lastName>Singh</lastName>
    </author>
    <collection role="institutes" number="1404">FG Softwareengineering</collection>
  </doc>
  <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>
