In remote healthcare monitoring, time series representation learning reveals critical patient behavior patterns from high-frequency data. This study analyzes home activity data from individuals living with dementia by proposing a two-stage, self-supervised learning approach tailored to uncover low-rank structures. The first stage converts time-series activities into text sequences encoded by a pre-trained language model, providing a rich, high-dimensional latent state space using a PageRank-based method. This PageRank vector captures latent state transitions, effectively compressing complex behaviour data into a succinct form that enhances interpretability. This low-rank representation not only enhances model interpretability but also facilitates clustering and transition analysis, revealing key behavioral patterns correlated with clinicalmetrics such as MMSE and ADAS-COG scores. Our findings demonstrate the framework's potential in supporting cognitive status prediction, personalized care interventions, and large-scale health monitoring.
View on arXiv@article{cui2025_2502.09173,
title={ Two-Stage Representation Learning for Analyzing Movement Behavior Dynamics in People Living with Dementia },
author={ Jin Cui and Alexander Capstick and Payam Barnaghi and Gregory Scott },
journal={arXiv preprint arXiv:2502.09173},
year={ 2025 }
}