CAREDAS : Context and Activity Recognition

Abstract

As the world population is growing older, more and more peoples are facing health issues. For elderly, leaving alone can be tough and risky, typically, a fall can have serious consequences for them. Con- sequently, smart homes are becoming more and more popular. Such sen- sors enriched environment can be exploited for health-care applications, in particular AnomalyDetection (AD).Currently,mostADsolutions only focus on detecting anomalies in the user daily activitieswhile omitting the ones from the environment itself. For instance the usermay have forgotten the pan on the stove while he/she is phoning. In this paper, we present a novel approach for detecting anomaly occurring in the home environment during user activities: CAREDAS. We propose a combination between ontologies and Markov Logic Network to classify the situations to anom- aly classes. Our system is implemented, tested and evaluated using real data obtained from the Hadaptic platform. Experimental results prove our approach to be efficient in terms of recognition rate.