Intelligent decision-making systems using wearable electronics and deep learning (DL) might identify Alzheimer's disease (AD) early for treatment. These technologies can continually monitor vital signs and behavioral characteristics to identify early cognitive deterioration in patients. Clinical examinations, neuroimaging, and cognitive testing are the main ways to identify Alzheimer's, but they are difficult, expensive, and frequently miss the illness early on. Such approaches lack the sensitivity and real-time monitoring essential for early intervention. Through wearable technology and sophisticated DL approaches, Early Detection using Deep Learning Algorithm (ED-DLA) tackles these constraints. In real time, wearable sensors capture data on heart rate, sleep habits, and physical activity. DL algorithms evaluate this data to identify early Alzheimer's. Continuous and non-invasive monitoring improves detection sensitivity and accuracy. To evaluate sequential wearable device data, the suggested technique uses an RNN-based image classification model. Temporal patterns are essential for understanding AD development, and the RNN does so well. The slight changes in cognitive and physical activities may indicate early-stage dementia. The suggested AD diagnosis and management system improves early detection accuracy and real-time monitoring, making it more dependable and scalable.
Sathish et al. (Thu,) studied this question.