Authors:
Georgios Siantikos
;
Theodoros Giannakopoulos
and
Stasinos Konstantopoulos
Affiliation:
NCSR Demokritos, Greece
Keyword(s):
Audio Analysis, Activities of Daily Living, Health Monitoring, Remote Monitoring, Audio Sensors, RaspberryPI, Audio Event Recognition.
Related
Ontology
Subjects/Areas/Topics:
Ambient Intelligence
;
Applications
;
Artificial Intelligence
;
Cardiovascular Imaging and Cardiography
;
Cardiovascular Technologies
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Pattern Recognition
;
Pervasive Technologies
;
Physiological Computing Systems
;
Signal Processing
;
Software Engineering
;
Symbolic Systems
Abstract:
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 singleboard
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.
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