A Model for Identifying the Behavior of Alzheimer’s Disease Patients in Smart Homes

In recent years, Smart Cities and Smart Homes have been studied as an important field of research. The design and construction of smart homes have flourished so that this technology is used for more comfort of people and helping their health. This technology is very useful for single and elderly people especially for Alzheimer’s disease patients who are alone at home and need permanent care. A lot of research has been done to detect the activity of users in smart homes. They gather raw data of sensors and use the usual classification algorithms for activity detection. The essence of the time dependency of sensors’ data has been ignored in most research, while considering this is very important, especially in the case of Alzheimer's patients. In this study, a Nonlinear AutoRegressive Network (NARX) is employed to detect the patient’s activity in a smart home. NARX is a recurrent dynamic network, which is commonly used in time-series modeling. The results show that the proposed model detects user activity, with an accuracy of 0.98. Since the high-risk behavior of the Alzheimer’s patient is very unknown; a fuzzy inference system is implemented based on the experience of the Alzheimer's sub-specialist and nurses. The main parameters were extracted and a 3-layers hierarchical fuzzy inference system was developed to detect and alarm the patient’s high-risk behavior without wearable sensors. The results show 98% accuracy in detecting the patient’s activity and 84% accuracy in determining its abnormality.