Executive Summary : | Alzheimer's disease (AD) is a common cause of dementia, characterized by a decline in cognitive function beyond normal aging. It affects memory, thinking, orientation, comprehension, calculation, learning capacity, language, and judgment. The disease has three general stages: preclinical AD, mild cognitive impairment (MCI), and dementia caused by AD. The preclinical stage is crucial for targeted risk reduction interventions. Prodromal changes of AD may occur earlier in some cognitive domains. However, AD can be curable and a better quality of life can be ensured if it can be predicted or detected early through 24x7 monitoring of physiological and neurological parameters. Chronic psychosocial stress can influence the onset and progression of AD, and other stress-associated disorders, such as depression, anxiety, and sleep disorders, have been associated with its development. AD can be detected through other physiological parameters, such as heart rate variability and gait patterns. Research suggests that the preclinical stage of AD includes physiological changes in the autonomic nervous system (ANs). This project proposes a four-layer, safety-critical, wearable framework using bio-multifunctional smart wearable sensors with edge-fog-cloud services to assess stress, gait, and other relevant physiological and neurological parameters of patients for remote prediction, early detection, and monitoring of AD in real-time. The framework will consist of bio-multifunctional wearable sensors, Wireless Body Area Networks (WBANs), and an AI-ML model for early prediction, detection, and monitoring of AD. |