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

黄逸然, Yexu Zhou, Haibin Zhao, Till Riedel, Michael Beigl
June, 2024
PDF Cite Project Source Document
Image credit: Unsplash

Abstract

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.

Type
Conference paper
Publication
In The International Joint Conference on Neural Networks
Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.
Create your slides in Markdown - click the Slides button to check out the example.

Add the publication’s full text or supplementary notes here. You can use rich formatting such as including code, math, and images.

Wearable Human Activity Recognition machine learning Deep Learning
黄逸然
黄逸然
Academic Associates

My research interests include Data Mining, XAI and Human Activity Recognition.