Two-Stage Representation Learning for Analyzing Movement Behavior Dynamics in People Living with Dementia

• URL: http://arxiv.org/abs/2502.09173v1
• Date: Thu, 13 Feb 2025 10:57:25 GMT
• Title: Two-Stage Representation Learning for Analyzing Movement Behavior Dynamics in People Living with Dementia
• Authors: Jin Cui, Alexander Capstick, Payam Barnaghi, Gregory Scott,
• Abstract summary: This study analyzes home activity data from individuals living with dementia by proposing a two-stage, self-supervised learning approach. The first stage converts time-series activities into text sequences encoded by a pre-trained language model. This PageRank vector captures latent state transitions, effectively compressing complex behaviour data into a succinct form.
• Score: 44.39545678576284
• License:
• 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.

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