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A Survey on Wearable Human Activity Recognition: Innovative Pipeline Development for Enhanced Research and Practice

Huang, Yiran ORCID iD icon 1,2; Zhou, Yexu 1,2; Zhao, Haibin ORCID iD icon 1,2; Riedel, Till ORCID iD icon 1,2; Beigl, Michael ORCID iD icon 1,2
1 Institut für Telematik (TM), Karlsruher Institut für Technologie (KIT)
2 Fakultät für Informatik (INFORMATIK), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

Recent trends in Wearable Human Activity Recognition (WHAR) have led to an unprecedented 42.9% increase in scholarly articles in 2022, underscoring the urgency for a comprehensive review to systematically categorize their varied research directions. Moreover, our analysis reveals that the contributions of current articles often deviate from the traditional stages of the human activity recognition pipeline, as established in prior literature. This misalignment suggests the necessity for an updated pipeline that more accurately reflects the intricacies and nuances of WHAR studies. In response, we review WHAR articles from 2021 to 2023 and introduce an innovative WHAR pipeline, emphasizing a research-focused approach. This new pipeline offers distinct advantages: it provides researchers with a clear and systematic categorization of WHAR articles, thereby enhancing understanding of the field. For practitioners, it facilitates the selection of customized methods for each stage, thereby optimizing final assembled model efficacy.


Preprint §
DOI: 10.5445/IR/1000169626
Veröffentlicht am 27.03.2024
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Telematik (TM)
Publikationstyp Proceedingsbeitrag
Publikationsmonat/-jahr 07.2024
Sprache Englisch
Identifikator KITopen-ID: 1000169626
Erschienen in 2024 IEEE International Joint Conference on Neural Networks (IJCNN 2024), Yokohama, 30th June - 5th July 2024
Veranstaltung International Joint Conference on Neural Networks (IJCNN 2024), Yokohama, Japan, 30.06.2024 – 05.07.2024
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Bemerkung zur Veröffentlichung in press
Schlagwörter wearable human activity recognition, machine learning, deep learning
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