Early anomaly detection in smart home: A causal association rule-based approach.

Abstract

As the world's population grows older, an increasing number of people are facing health issues. For the elderly, living alone can be difficult and dangerous. Consequently, smart homes are becoming increasingly popular. A sensor-rich environment can be exploited for healthcare applications, in particular, anomaly detection (AD). The literature review for this paper showed that few works consider environmental factors to detect anomalies. Instead, the focus is on user activity and checking whether it is abnormal, i.e., does not conform to expected behavior. Furthermore, reducing the number of anomalies using early detection is a major issue in many applications. In this context, anomaly-cause discovery may be helpful in recommending actions that may prevent risk. In this paper, we present a novel approach for detecting the risk of anomalies occurring in the environment regarding user activities. The method relies on anomaly-cause extraction from a given dataset using causal association rules mining. These anomaly causes are utilized afterward for real-time analysis to detect the risk of anomalies using the Markov logic network machine learning method. The detected risk allows the method to recommend suitable actions to perform in order to avoid the occurrence of an actual anomaly. The proposed approach is implemented, tested, and evaluated for each contribution using real data obtained from an intelligent environment platform and real data from a clinical datasets. Experimental results prove our approach to be efficient in terms of recognition rate.