Automatic extraction of an effective rule set for fall detection for a real-time mobile monitoring system

Automatic fall detection is a major issue in taking care of the health of elderly people. In this task the capability of telling in real time falls from normal daily activities is crucial. To this aim, this paper proposes an approach based on the automatic extraction of knowledge expressed as a set of IFTHEN rules from a database of fall recordings. This set of rules, generated offline, can then be exploited in a real-time mobile monitoring system: data gathered by wearable sensors are processed in real time and, if their values activate some of the rules describing falls, an alarm message is automatically produced. The approach has been compared against other classifiers on a real-world fall database, and its discrimination ability is shown to be higher. Moreover, a test phase for the real-time mobile monitoring system is being carried out over real cases.