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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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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Siantikos, Georgios</dc:creator>
  <dc:creator>Giannakopoulos, Theodoros</dc:creator>
  <dc:creator>Konstantopoulos, Stasinos</dc:creator>
  <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: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>
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