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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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  <dc:creator>Siantikos, Georgios</dc:creator>
  <dc:creator>Giannakopoulos, Theodoros</dc:creator>
  <dc:creator>Konstantopoulos, Stasinos</dc:creator>
  <dc:date>2017-07-20</dc:date>
  <dc:description>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.
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.</dc:description>
  <dc:identifier>https://zenodo.org/record/835854</dc:identifier>
  <dc:identifier>10.1007/978-3-319-62704-5_2</dc:identifier>
  <dc:identifier>oai:zenodo.org:835854</dc:identifier>
  <dc:publisher>Springer</dc:publisher>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/643892/</dc:relation>
  <dc:relation>url:https://link.springer.com/chapter/10.1007%2F978-3-319-62704-5_2</dc:relation>
  <dc:relation>url:https://www.researchgate.net/publication/318550233</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/radio</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/roboskel</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:subject>audio analysis</dc:subject>
  <dc:subject>activities of daily living</dc:subject>
  <dc:subject>health monitoring</dc:subject>
  <dc:subject>remote monitoring</dc:subject>
  <dc:subject>audio event recognition</dc:subject>
  <dc:title>Monitoring activities of daily living using audio analysis and a RaspberryPI: A use case on bathroom activity monitoring</dc:title>
  <dc:type>info:eu-repo/semantics/bookPart</dc:type>
  <dc:type>publication-section</dc:type>
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