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2024 | OriginalPaper | Chapter

Re-thinking Human Activity Recognition with Hierarchy-Aware Label Relationship Modeling

Authors : Jingwei Zuo, Hakim Hacid

Published in: Advances in Knowledge Discovery and Data Mining

Publisher: Springer Nature Singapore

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Abstract

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.

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Metadata
Title
Re-thinking Human Activity Recognition with Hierarchy-Aware Label Relationship Modeling
Authors
Jingwei Zuo
Hakim Hacid
Copyright Year
2024
Publisher
Springer Nature Singapore
Book
Advances in Knowledge Discovery and Data Mining

Print ISBN: 978-981-9722-64-8

Electronic ISBN: 978-981-9722-62-4

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-981-97-2262-4_1

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