Searching for temporal patterns in AmI sensor data

Abstract

Anticipation is a key property of human-human communication, and it is highly desirable for ambient environments to have the means of anticipating events to create a feeling of responsiveness and intelligence in the user. In a home or work environment, a great number of low-cost sensors can be deployed to detect simple events: the passing of a person, the usage of an object, the opening of a door. The methods that try to discover re-usable and interpretable patterns in temporal event data have several shortcomings. Using a testbed that we have developed for this purpose, we first contrast current approaches to the problem. We then extend the best of these approaches, the T-Pattern algorithm, with Gaussian Mixture Models, to obtain a fast and robust algorithm to find patterns in temporal data. Our algorithm can be used to anticipate future events, as well as to detect unexpected events as they occur.