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Monitoring activities of daily living using audio analysis and a RaspberryPI: A use case on bathroom activity monitoring

Siantikos, Georgios; Giannakopoulos, Theodoros; Konstantopoulos, Stasinos

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      <creatorName>Siantikos, Georgios</creatorName>
      <affiliation>NCSR "Demokritos"</affiliation>
      <creatorName>Giannakopoulos, Theodoros</creatorName>
      <affiliation>NCSR "Demokritos"</affiliation>
      <creatorName>Konstantopoulos, Stasinos</creatorName>
      <affiliation>NCSR "Demokritos"</affiliation>
    <title>Monitoring activities of daily living using audio analysis and a RaspberryPI: A use case on bathroom activity monitoring</title>
    <subject>audio analysis</subject>
    <subject>activities of daily living</subject>
    <subject>health monitoring</subject>
    <subject>remote monitoring</subject>
    <subject>audio event recognition</subject>
    <date dateType="Issued">2017-07-20</date>
  <resourceType resourceTypeGeneral="Text">Book section</resourceType>
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    <relatedIdentifier relatedIdentifierType="URL" relationType="IsIdenticalTo"></relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsIdenticalTo"></relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1007/978-3-319-62704-5_2</relatedIdentifier>
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    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;A framework that utilizes audio information for recognition of activities of daily living (ADLs) in the context of a health monitoring environment is presented in this chapter. We propose integrating a Raspberry PI single-board PC that is used both as an audio acquisition and analysis unit. So Raspberry PI captures audio samples from the attached microphone device and executes a set of real-time feature extraction and classification procedures, in order to provide continuous and online audio event recognition to the end user.&lt;br&gt;
Furthermore, a practical workflow is presented, that helps the technicians that setup the device to perform a fast, user-friendly and robust tuning and calibration procedure. As a result, the technician is capable of "training"' the device without any need for prior knowledge of machine learning techniques. The proposed system has been evaluated against a particular scenario that is rather important in the context of any healthcare monitoring system for the elder: In particular, we have focused on the "bathroom scenario" according to which, a Raspberry PI device equipped with a single microphone is used to monitor bathroom activity on a 24/7 basis in a privacy-aware manner, since no audio data is stored or transmitted. The presented experimental results prove that the proposed framework can be successfully used for audio event recognition tasks.&lt;/p&gt;</description>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/643892/">643892</awardNumber>
      <awardTitle>Robots in assisted living environments: Unobtrusive, efficient, reliable and modular solutions for independent ageing</awardTitle>
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