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Association for Computing Machinery (ACM), ACM Computing Surveys, 8(54), p. 1-34, 2021

DOI: 10.1145/3472290

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A Survey on Deep Learning for Human Activity Recognition

Journal article published in 2021 by Fuqiang Gu ORCID, Mu-Huan Chung, Mark Chignell, Shahrokh Valaee, Baoding Zhou, Xue Liu
This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Abstract

Human activity recognition is a key to a lot of applications such as healthcare and smart home. In this study, we provide a comprehensive survey on recent advances and challenges in human activity recognition (HAR) with deep learning. Although there are many surveys on HAR, they focused mainly on the taxonomy of HAR and reviewed the state-of-the-art HAR systems implemented with conventional machine learning methods. Recently, several works have also been done on reviewing studies that use deep models for HAR, whereas these works cover few deep models and their variants. There is still a need for a comprehensive and in-depth survey on HAR with recently developed deep learning methods.