  Human Activity Recognition (HAR) has been studied for decades, from data
collection, learning models, to post-processing and result interpretations.
However, the inherent hierarchy in the activities remains relatively
under-explored, despite its significant impact on model performance and
interpretation. In this paper, we propose H-HAR, by rethinking the HAR tasks
from a fresh perspective by delving into their intricate global label
relationships. Rather than building multiple classifiers separately for
multi-layered activities, we explore the efficacy of a flat model enhanced with
graph-based label relationship modeling. Being hierarchy-aware, the graph-based
label modeling enhances the fundamental HAR model, by incorporating intricate
label relationships into the model. We validate the proposal with a multi-label
classifier on complex human activity data. The results highlight the advantages
of the proposal, which can be vertically integrated into advanced HAR models to
further enhance their performances.
