Two-Stage Representation Learning for Analyzing Movement Behavior Dynamics in People Living with Dementia
Journal:
arXiv
Published Date:
Feb 13, 2025
Abstract
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.
