Design and Deployment of Multi-Modal Federated Learning Systems for Alzheimer's Disease Monitoring
Xiaomin Ouyang
Abstract
Alzheimer's Disease (AD) and related dementia are a growing global health challenge due to the aging population. The prominence of mobile devices and recent breakthroughs in machine learning have enabled an emerging class of new AI-powered health systems for applications like Alzheimer's Disease monitoring. In this paper, we present the first end-to-end system that integrates multi-modal sensors and federated learning algorithms for detecting multidimensional AD digital biomarkers in natural living environments. We recognize several major challenges in designing such a real-world federated learning system, including limited data labels, data heterogeneity, and limited computing resources. We built a compact multi-modality hardware system and deployed it in a four-week clinical trial involving 61 elderly participants. The results indicate that our system can accurately detect a comprehensive set of digital biomarkers with up to 95% accuracy and identify AD with an average of 87.5% accuracy.