Time-series representation learning is a key area of research for remote healthcare monitoring applications. In this work, we focus on a dataset of recordings of in-home activity from people living with Dementia. We design a representation learning method based on converting activity to text strings that can be encoded using a language model fine-tuned to transform data from the same participants within a $30$-day window to similar embeddings in the vector space. This allows for clustering and vector searching over participants and days, and the identification of activity deviations to aid with personalised delivery of care.

翻译：时间序列表示学习是远程健康监测应用的一个关键研究领域。本研究聚焦于阿尔茨海默病患者居家活动记录数据集。我们设计了一种表示学习方法，其核心是将活动数据转换为文本字符串，进而通过语言模型进行编码；该语言模型经过微调，能够将同一参与者在$30$天窗口内的数据映射为向量空间中相似的嵌入表示。该方法支持跨参与者与时间维度的聚类与向量检索，并可通过识别活动偏差为个性化护理提供辅助。