NeuroSense: Smartwatch-based Early Detection Framework for Alzheimer’s Disease

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder with an annual global economic impact of approximately $ \$ $ 1 trillion. Early diagnosis is crucial to mitigate disease progression, yet current detection methodologies lack consistency and accuracy in delineating disease stages. To address these challenges, we introduce NeuroSense, a novel smartwatchbased early detection framework for AD. NeuroSense employs advanced activity recognition algorithms to passively monitor ambulatory patterns, behaviors, and sleep cycles via Inertial Measurement Unit (IMU) and photoplethysmogram (PPG) sensors from smartwatches (i.e., Apple Watch and Fitbit), including metrics like walking speed, sleep posture, and heart rate variability. NeuroSense leverages enhanced shallow neural networks integrating cross-sectional entropy to dynamically adjust feature weights, improving sensitivity to subtle variations in AD biomarkers. Additionally, the SqueezeNet architecture has been optimized by introducing deeper fire modules and expanding filter sizes, enabling high-resolution feature extraction with reduced computational overhead. These models are further refined with a cloud computing infrastructure, allowing model updation, ensuring adaptive learning based on continuous data influx. To validate its efficacy, NeuroSense was tested on AI-generated datasets, achieving a detection accuracy of 94.4%, substantially higher than existing approaches. This novel framework provides a robust, scalable, and non-invasive solution for continuous AD monitoring, representing a significant advancement in the early detection and management of neurodegenerative disorders.