Reasoning Support for Risk Prediction and Prevention in Independent
  Living
  In recent years there has been growing interest in solutions for the delivery
of clinical care for the elderly, due to 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 SINDI (Secure and INDependent lIving), focused on i) collecting a
limited amount of data about the person and the environment through Wireless
Sensor Networks (WSN), and ii) 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.
