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Reasoning support for risk prediction and prevention in independent living

Published online by Cambridge University Press:  28 January 2011

A. MILEO ,
D. MERICO  and
R. BISIANI
A. MILEO
Affiliation:
NOMADIS Research Lab. Department of Informatics, Systems and Communication University of Milan-Bicocca, Viale Sarca 336/14, I–20126 Milan, Italy (e-mail: [email protected], [email protected], [email protected])
D. MERICO
Affiliation:
NOMADIS Research Lab. Department of Informatics, Systems and Communication University of Milan-Bicocca, Viale Sarca 336/14, I–20126 Milan, Italy (e-mail: [email protected], [email protected], [email protected])
R. BISIANI
Affiliation:
NOMADIS Research Lab. Department of Informatics, Systems and Communication University of Milan-Bicocca, Viale Sarca 336/14, I–20126 Milan, Italy (e-mail: [email protected], [email protected], [email protected])
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Abstract

In recent years there has been a growing interest in solutions for the delivery of clinical care for the elderly because of the large increase in aging population. Monitoring a patient in his home environment is necessary to ensure continuity of care in home settings, but, to be useful, this activity must not be too invasive for patients and a burden for caregivers. We prototyped a system called Secure and INDependent lIving (SINDI), focused on (a) collecting a limited amount of data about the person and the environment through Wireless Sensor Networks (WSN), and (b) inferring from these data enough information to support caregivers in understanding patients' well-being and in predicting possible evolutions of their health. Our hierarchical logic-based model of health combines data from different sources, sensor data, tests results, common-sense knowledge and patient's clinical profile at the lower level, and correlation rules between health conditions across upper levels. The logical formalization and the reasoning process are based on Answer Set Programming. The expressive power of this logic programming paradigm makes it possible to reason about health evolution even when the available information is incomplete and potentially incoherent, while declarativity simplifies rules specification by caregivers and allows automatic encoding of knowledge. This paper describes how these issues have been targeted in the application scenario of the SINDI system.

Keywords

answer set programmingwireless sensor networksindependent livingpredictioncontext-awarenessknowledge representationdependency graph
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 11 , Special Issue 2-3: The 24th International Conference on Logic Programming (ICLP 2008) , March 2011 , pp. 361 - 395
Copyright
Copyright © Cambridge University Press 2011

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