  Complex activities of daily living (ADLs) often consist of multiple
micro-activities. When performed sequentially, these micro-activities help the
user accomplish the broad macro-activity. Naturally, a deeper understanding of
these micro-activities can help develop more sophisticated human activity
recognition (HAR) models and add explainability to their inferred conclusions.
Previous research has attempted to achieve this by utilizing fine-grained
annotated data that provided the required supervision and rules for associating
the micro-activities to identify the macro-activity. However, this
``bottom-up'' approach is unrealistic as getting such high-quality,
fine-grained annotated sensor datasets is challenging, costly, and
time-consuming. Understanding this, in this paper, we develop AmicroN, which
adapts a ``top-down'' approach by exploiting coarse-grained annotated data to
expand the macro-activities into their constituent micro-activities without any
external supervision. In the backend, AmicroN uses \textit{unsupervised}
change-point detection to search for the micro-activity boundaries across a
complex ADL. Then, it applies a \textit{generalized zero-shot} approach to
characterize it. We evaluate AmicroN on two real-life publicly available
datasets and observe that AmicroN can identify the micro-activities with micro
F\textsubscript{1}-score $>0.75$ for both datasets. Additionally, we also
perform an initial proof-of-concept on leveraging the state-of-the-art (SOTA)
large language models (LLMs) with attribute embeddings predicted by AmicroN to
enhance further the explainability surrounding the detection of
micro-activities.
