Smart Home based Prediction of Symptoms of Alzheimer’s Disease using Machine Learning and Contextual Approach

Alzheimer’s disease is one of the most prevailing diseases in elderly society that leads to memory loss affecting their daily living. In this paper, an automated intelligent system is proposed to predict the multi-modal symptoms of Alzheimer’s disease in order to offer appropriate actions during critical situation. To model this system machine learning techniques and contextual approach is preferred. Smart home and an intelligent system are employed to predict the symptoms of Alzheimer’s disease with the help of sensors. In existing work, validation in terms of cognitive, mobility and depression states of the older adults were done using activity recognition. But the prediction of Mood plays a vital role among the multi-modal symptoms. Thus the proposed system in addition to cognitive also uses anxiety and depression states of the older adults’ together helps in predicting the multi-modal symptoms. The novelty of the proposed system deals with the contextual based analysis to predict the mood using ontology approach in addition to the statistical based analysis. Using these techniques, the system measures the health assessment scores and detects a reliable change based on the assessment points in a proficient way.