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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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{
  "publisher": "Springer", 
  "DOI": "10.1007/978-3-319-62704-5_2", 
  "container_title": "Revised Selected Papers of the 2nd Second International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2016). Communications in Computer and Information Science, vol. 736", 
  "title": "Monitoring activities of daily living using audio analysis and a RaspberryPI: A use case on bathroom activity monitoring", 
  "issued": {
    "date-parts": [
      [
        2017, 
        7, 
        20
      ]
    ]
  }, 
  "abstract": "<p>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.<br>\nFurthermore, 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.</p>", 
  "author": [
    {
      "family": "Siantikos, Georgios"
    }, 
    {
      "family": "Giannakopoulos, Theodoros"
    }, 
    {
      "family": "Konstantopoulos, Stasinos"
    }
  ], 
  "publisher_place": "Germany", 
  "type": "chapter", 
  "id": "835854"
}
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