Background: Sensor-based remote health monitoring is increasingly used to detect adverse health in people living with dementia (PLwD) at home, aiming to prevent hospitalizations and reduce caregiver burden. However, home sensor data is often noisy, overly granular, and suffers from unreliable labeling, data drift and high variability between households. Current anomaly detection methods lack generalizability and personalization, often requiring anomaly-free training data and frequent model updates.

Objective: To develop a lightweight, explainable, self-supervised approach with personalized alert thresholds to detect adverse health events in PLwD, using changes in home activity.

Methods: We hypothesized that health downturns manifest as detectable shifts in household movement patterns. Our approach leverages a Graph Barlow Twins contrastive model, which uses granular activity data and a macroscopic view to extract noise-robust, high-level and low-level discriminative features that represent daily activity patterns. Household-personalized alert thresholds are calculated based on clinician-set target alert rates, and daily anomaly scores are compared against these thresholds, triggering alerts for the clinical monitoring team. Model attention weights support explainability. Data were collected from a real-world dataset by the UK Dementia Research Institute (August 2019-April 2022).

Results: Our model outperformed state-of-the-art temporal graph algorithms in detecting agitation and fall events across three patient cohorts, achieving 81% average recall and 88% generalizability at a target alert rate of 7%.

Conclusion: We developed a novel, lightweight, explainable, and personalized Graph Barlow Twins model for real-world remote health monitoring in dementia care, with potential for broader applications in healthcare and sensor-based environments.