Re-thinking Human Activity Recognition with Hierarchy-aware Label Relationship Modeling
This work addresses the problem of improving model performance and interpretation in HAR for researchers and practitioners, but appears incremental as it builds on existing HAR models with a new label relationship approach.
The paper tackles the problem of Human Activity Recognition (HAR) by addressing the under-explored hierarchy in activities, proposing H-HAR, a flat model enhanced with graph-based label relationship modeling. The results show advantages that can be integrated into advanced HAR models to enhance performance.
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.