Re-thinking Human Activity Recognition with Hierarchy-aware Label Relationship Modeling
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911792370810880 |
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| author | Zuo, Jingwei Hacid, Hakim |
| author_facet | Zuo, Jingwei Hacid, Hakim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_05557 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Re-thinking Human Activity Recognition with Hierarchy-aware Label Relationship Modeling Zuo, Jingwei Hacid, Hakim Signal Processing Human-Computer Interaction Machine Learning 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. |
| title | Re-thinking Human Activity Recognition with Hierarchy-aware Label Relationship Modeling |
| topic | Signal Processing Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2403.05557 |