Monitoring Activities of Daily Living Using Audio Analysis and a RaspberryPI: A Use Case on Bathroom Activity Monitoring
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.
KeywordsAudio analysis Activities of daily living Health monitoring Remote monitoring Audio sensors RaspberryPI Audio event recognition
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 643892. Please see http://www.radio-project.eu for more details.
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