• Medientyp: Sonstige Veröffentlichung; E-Artikel; Elektronischer Konferenzbericht
  • Titel: A Survey on Wearable Human Activity Recognition: Innovative Pipeline Development for Enhanced Research and Practice
  • Beteiligte: Huang, Yiran [Verfasser:in]; Zhou, Yexu [Verfasser:in]; Zhao, Haibin [Verfasser:in]; Riedel, Till [Verfasser:in]; Beigl, Michael [Verfasser:in]
  • Erschienen: Institute of Electrical and Electronics Engineers, 2024-03-27
  • Sprache: Englisch
  • DOI: https://doi.org/10.5445/IR/1000169626
  • Schlagwörter: DATA processing & computer science ; machine learning ; wearable human activity recognition ; deep learning
  • Entstehung:
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  • Beschreibung: 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.
  • Zugangsstatus: Freier Zugang