IoT-based intelligent system For Alzheimer's Disease Detection & Monitoring

Abstract

This research is based on two areas related to Alzheimer's disease, the first is the early detection and diagnosis of Alzheimer's disease using deep learning techniques and its various algorithms, and the second relates to how to monitor and follow up on Alzheimer's disease using the Internet of Things (IOT). In this paper, a new diagnosis based on deep machine learning and monitoring of diseases similar to Alzheimer's is proposed. Diagnosis of Alzheimer's-like diseases is achieved through deep learning magnetic resonance imaging (MRI) analysis followed by an activity tracking framework to monitor people's activities in daily life using wearable inertial sensors. Activity monitoring provides a framework for assistance in activities of daily living and assessment of patient deterioration based on activity level. The results of Alzheimer's diagnosis show an improvement of up to 86.34% with respect to current known techniques. Furthermore, greater than 95% accuracy was achieved for classifying activities of daily living, which is very encouraging in looking at the subject's activity profile.