Hybrid learning architecture for unobtrusive infrared tracking support

The system architecture presented in this paper is designed for helping an aged person to live longer independently in their own home by detecting unusual and potentially hazardous behaviours. The system consists of two major components. The first component is the tracking part which is responsible for monitoring the movements of the person within the home, while the second part is a learning agent which is responsible for learning the behavioural patterns of the person. For the tracking part of the system a simulation portraying a virtual room with passive infrared sensors has been designed, while for the learning agent a hybrid architecture has been implemented. The hybrid architecture consists of a Markov Chain Model, Template Matching, Fuzzy Logic and Memory-Based reasoning techniques. The hybrid structure was selected because it combined the strengths of the constituent algorithms and because it supports the learning with limited training data. The resultant system was able to not only classify between the normal and the abnormal paths but was also able to distinguish between different normal routes. We claim that passive infrared tracking combined with a hybrid learning architecture has potential for adaptive unobtrusive tracking support.