Abstract
This paper describes a preliminary study of an activity monitoring system for a residential house based on Bayesian networks. For the demonstrative purpose, a proof-of-concept system using vibration measurement has been proposed, in which specific physical features of the vibration that may indicate either daily activities of residents or other disturbances such as traffic-induced vibration are extracted from the collected data. Vibration data is classified into two groups based on these features, and the attribution degree was defined based on the support vector machines associated with the extracted data classes. A Bayesian Network is designed as the data model that represents the relevance of the data classes to external variables such as time and in/out status status. Finally an abnormality index has been proposed and investigated based on the likelihood evaluated on the trained Bayesian network.
