  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.
