用两阶段自监督学习分析痴呆患者居家行为，揭示认知状态关联模式。
Two-Stage Representation Learning for Analyzing Movement Behavior Dynamics in People Living with Dementia
- 先将时间序列行为转为文本，再用图排序法提取低秩隐状态
- 压缩后表示能有效聚类与转移分析，与MMSE/ADAS-COG评分相关
- 适合临床认知评估、个性化照护与远程健康监测场景
在远程医疗监测中，时间序列表征学习可从高频数据中揭示关键患者行为模式。本研究针对痴呆患者居家活动数据，提出一种两阶段自监督学习方法，旨在挖掘低秩结构。第一阶段将时间序列行为转换为由预训练语言模型编码的文本序列，利用基于PageRank的方法生成高维潜在状态空间，其向量捕捉潜在状态转移，有效将复杂行为数据压缩为简洁形式，提升可解释性。该低秩表示不仅增强模型可解释性，还促进聚类与转移分析，揭示与临床指标（如MMSE和ADAS-COG评分）相关的行为模式。研究结果表明，该框架在支持认知状态预测、个性化照护干预及大规模健康监测方面具有潜力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。