Re-thinking Human Activity Recognition with Hierarchy-aware Label Relationship Modeling
<|reference_start|>Re-thinking Human Activity Recognition with Hierarchy-aware Label Relationship Modeling: 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.<|reference_end|>
@article{zuo2024re-thinking,
    title={Re-thinking Human Activity Recognition with Hierarchy-aware Label
  Relationship Modeling},
    author={Jingwei Zuo and Hakim Hacid},
    journal={arXiv preprint arXiv:2403.05557},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05557},
    primaryClass={eess.SP cs.HC cs.LG}
}