Analyzing Patient Daily Movement Behavior Dynamics Using Two-Stage Encoding Model
Journal:
arXiv
Published Date:
Feb 14, 2025
Abstract
In the analysis of remote healthcare monitoring data, time series
representation learning offers substantial value in uncovering deeper patterns
of patient behavior, especially given the fine temporal granularity of the
data. In this study, we focus on a dataset of home activity records from people
living with Dementia. We propose a two-stage self-supervised learning approach.
The first stage involves converting time-series activities into text strings,
which are then encoded by a fine-tuned language model. In the second stage,
these time-series vectors are bi-dimensionalized for applying PageRank method,
to analyze latent state transitions to quantitatively assess participants
behavioral patterns and identify activity biases. These insights, combined with
diagnostic data, aim to support personalized care interventions.
