Monitoring behavior with an array of sensors

Abstract

The objective is to detect activities taking place in a home and to create a model of behavior for the occupant. A behavior is a pattern in the sequence of activities. An array of sensors captures the status of appliances. Models for the occupant's activities are built from the captured data using supervised and unsupervised learning techniques. The models of behavior are built using the hidden Markov model (HMM) technique. Predictive models can be used in a number of ways: to enhance user experience, to maximize resource usage efficiency, for safety and security. This work focuses on supporting independent living and enhancing quality of life of older persons. The ultimate goal is for the system to distinguish between normal and anomalous behavior. In this paper, we present the results of comparing supervised and unsupervised classification techniques applied to the problem of modeling activity for the purpose of modeling behavior in a home. © 2007 Blackwell Publishing, Inc.