Ambient Intelligent Monitoring of Dementia Suffers Using Unsupervised Neural Networks and Weighted Rule Based Summarisation

This paper investigates the development of a system for monitoring of dementia suffers living in their own homes. The system uses unobtrusive pervasive sensor and actuator devices that can be deployed within a patient's home grouped and accessed via standardized platforms. For each sensor group our system uses unsupervised neural networks to identify the patient's habitual behaviours based on their activities in the environment. Rule-based summarisation is used to provide descriptive rules representing the intra and inter activity variations within the discovered behaviours. We propose a model comparison mechanism to facilitate tracking of behaviour changes, which could be due to the effects of cognitive decline. We demonstrate using user data acquired from a real pervasive computing environment, how our system is able to identify the user's prominent behaviours enabling assessment and future tracking.