Abstract
<jats:p>Activities of daily living (ADLs) provide important indicators of functional decline in people living with dementia, motivating the need for continuous in-home monitoring. However, deploying transformer-based activity recognition models on resource-constrained edge devices remains challenging because of limited computational resources and the need to preserve participant privacy by avoiding cloud-based processing. In this work, we propose a room-specialized Mixture-of-Experts (MoE) architecture for edge-based ADL recognition using ambient smart home sensors. Household activities are decomposed into room-specific transformer experts through deterministic routing, while temporal subsampling bounds the computational cost of each activity segment, enabling efficient on-device learning and inference. We evaluated the proposed framework using data collected from five dementia households, achieving Macro-F1 scores ranging from 0.437 to 0.911 despite substantial differences in activity distributions across homes. End-to-end training was successfully performed on a Raspberry Pi, demonstrating the feasibility of transformer-based ADL recognition on low-cost edge hardware. These findings suggest that room-specialized MoE provides a practical, privacy-preserving framework for continuous smart home monitoring and establishes a foundation for future edge-native healthcare applications, including continual and federated learning</jats:p>