Keeping elderly people at home: A multi-agent classification of monitoring data

Abstract

We propose software architecture to monitor elderly people in their own homes. We want to build patterns of monitored people dynamically from data about activity, movements and physiological information. To obtain this macroscopic view, we use a multi-agent method of classification: every agent has a simple skill of classification. They generate partial partitions and cooperate to obtain a set of patterns. The patterns are used at a personal level, for example to raise an alert, but also to evaluate global risks. These data are dynamic; the system has to maintain the built patterns and has to create new patterns. Therefore, the system is adaptive and can be spread on a large scale. © 2008 Springer-Verlag Berlin Heidelberg.