Health-Status Monitoring Through Analysis of Behavioral Patterns

TLDR

The growing elderly population requires support for independent, healthy home living rather than costly institutional care. The study aimed to determine whether an ambient intelligence monitoring system could detect and report elder behavioral patterns. The SmartHouse prototype employs basic in‑home sensors and mixture‑model probabilistic analysis to detect behavioral patterns. The mixture‑model analysis of the occupant’s event log successfully identified behavioral patterns.

Abstract

With the rapid growth of the elderly population, there is a need to support the ability of elders to maintain an independent and healthy lifestyle in their homes rather than through more expensive and isolated care facilities. One approach to accomplish these objectives employs the concepts of ambient intelligence to remotely monitor an elder's activities and condition. The SmartHouse project uses a system of basic sensors to monitor a person's in-home activity; a prototype of the system is being tested within a subject's home. We examined whether the system could be used to detect behavioral patterns and report the results in this paper. Mixture models were used to develop a probabilistic model of behavioral patterns. The results of the mixture-model analysis were then evaluated by using a log of events kept by the occupant.