Abstract
Abstract Background Sensor‐based remote health monitoring of persons living with dementia (PLwD) can be used to monitor the progression of their condition with minimal intrusion. This helps minimize preventable hospital admissions, while allowing researchers to improve their understanding of dementia. Existing approaches for detecting anomalies in activity and behavior in PLwD are challenged by data noise, drift, label reliance, and poor explainability and generalizability due to wide inter‐individual variability. Method We propose and evaluate a solution that combines graph learning with the Contextual Matrix Profile (CMP), an ultra‐fast distance‐based discord detection technique. Daily household movement data collected via passive infrared sensors are used to generate multivariate CMP. This is encoded as a set of time‐evolving graphs. A self‐supervised graph model is trained to separate graphs by distance, so discordant time blocks show up as anomalous graphs. Differences between successive graph embeddings are subject to moving average thresholding to detect anomaly. We compare our method to four state‐of‐the‐art outlier detection algorithms, and demonstrate the generalizability of a distance‐based graph model trained on one cohort, to anomaly detection in unseen individuals. Result Graph‐enhanced CMP models outperform state‐of‐the‐art algorithms, yielding 74% recall with a 5.7% alert rate and 88% household‐level validity when evaluated on 13,426 days’ data from 42 households with 145 agitation events, collected by the UK Dementia Research Institute between August 2019 and April 2022. Similar results are obtained for a 23‐participant, 5284‐day Falls cohort: 72% recall, 5.85% alert rate and 83% household‐level validity. Pretrained models yield similarly high performance for unseen households in both cohorts. Experiments on the publicly available PAMAP dataset reveal superior top‐k alert performance. Explainability is achieved via the CMP that is the source for graphs. Conclusion We address the need for generalizable anomaly detection models for multivariate time series sensor data in remote health monitoring. While the CMP allows the ability to denoise and detect patterns at a household level, a graph model constructed in the distance space is generalizable across households. With higher sensitivity, fewer alerts than state‐of‐the‐art methods and inherent explainability, graph‐CMP models offer a clinically meaningful unsupervised anomaly detection technique for dementia and beyond.