Analyzing Patient Daily Movement Behavior Dynamics Using Two-Stage Encoding Model

2025/02/14 by Jin Cui, Alexander Capstick, Cui, Jin +5
Medicine · Neuroscience · #Advanced MRI Techniques and Applications #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Functional Brain Connectivity Studies #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2502.09947

openalex publication_date 2025/02/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Related

• Adversarial Unsupervised Representation Learning for Activity\n Time-Series
• PersonaDrift: A Benchmark for Temporal Anomaly Detection in Language-Based Dementia Monitoring
• Benchmarking Early Agitation Prediction in Community-Dwelling People with Dementia Using Multimodal Sensors and Machine Learning
• Actionable Interpretation of Machine Learning Models for Sequential Data: Dementia-related Agitation Use Case
• Semi-supervised Learning for Identifying the Likelihood of Agitation in People with Dementia