Intelligent Abnormal Residents' Behavior Detection in Smart Homes for Risk Management using Fuzzy Logic Algorithm

In recent years, the population of sick and elderly people who are alone and need care has increased. This issue increases the need to have a smart home to be aware of the patient's condition. Identifying the patient's activity using sensors embedded in the environment is the first step to reach a smart home where the people around the patient can leave the patient alone at home with less worry. In literature, a variety of methods for detecting the performance of users in the smart home are discussed. In this study, a method for abnormal behavior detection and identifying the level of risk is proposed, in which fuzzy logic is used in cases such as when the activity start. Experimental results demonstrates that the proposed method achieved satisfied performance with 90% accuracy rate that presented better results compared to other existing methods.