  Human Activity Recognition is a time-series analysis problem. A popular
analysis procedure used by the community assumes an optimal window length to
design recognition pipelines. However, in the scenario of smart homes, where
activities are of varying duration and frequency, the assumption of a constant
sized window does not hold. Additionally, previous works have shown these
activities to be made up of building blocks. We focus on identifying these
underlying building blocks--structural constructs, with the use of large
language models. Identifying these constructs can be beneficial especially in
recognizing short-duration and infrequent activities. We also propose the
development of an activity recognition procedure that uses these building
blocks to model activities, thus helping the downstream task of activity
monitoring in smart homes.
