A Low-cost Approach for Detecting Activities of Daily Living using Audio Information: A Use Case on Bathroom Activity Monitoring

In this paper, we present an architecture for recognizing events related to activities of daily living in the context of a health monitoring environment. The proposed approach explores the integration of a Raspberry PI single-board PC both as an audio acquisition and analysis unit. A set of real-time feature extraction and classification procedures has been implemented and integrated on the Raspberry PI device, in order to provide continuous and online audio event recognition. In addition, a tuning and calibration workflow is presented, according to which the technicians installing the device in a fast ans user-friendly manner, without any requirements for machine learning expertise. The proposed approach has been evaluated against a particular scenario that is rather important in the context of any healthcare monitoring system for the elder, namely the "bathroom scenario" according to which a single microphone installed on a Raspberry PI device is used to monitor bathroom activity in a 24/7 basis. Experimental results indicate a satisfactory performance rate on the classification process (around 70% for five bathroom-related audio classes) even when less than two minutes of annotated data are used for training in the installation procedure. This makes the whole procedure non demanding in terms of time and effort needed to be calibrated by the technician.