PreprintHypothesisVersion 3Preserved in Portico This version is not peer-reviewed
Human Activity Recognition with Deep Learning: Overview, Challenges & Possibilities
Pranjal Kumar
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Version 1
: Received: 13 February 2021 / Approved: 17 February 2021 / Online: 17 February 2021 (09:26:44 CET)
Version 2
: Received: 1 June 2021 / Approved: 1 June 2021 / Online: 1 June 2021 (14:57:20 CEST)
Version 3
: Received: 1 June 2021 / Approved: 2 June 2021 / Online: 2 June 2021 (09:23:40 CEST)
Version 4
: Received: 4 June 2021 / Approved: 7 June 2021 / Online: 7 June 2021 (13:04:11 CEST)
Kumar, P., Chauhan, S. Human activity recognition with deep learning: overview, challenges and possibilities. CCF Trans. Pervasive Comp. Interact. (2021). https://doi.org/10.1007/s42486-021-00063-5
Kumar, P., Chauhan, S. Human activity recognition with deep learning: overview, challenges and possibilities. CCF Trans. Pervasive Comp. Interact. (2021). https://doi.org/10.1007/s42486-021-00063-5
Kumar, P., Chauhan, S. Human activity recognition with deep learning: overview, challenges and possibilities. CCF Trans. Pervasive Comp. Interact. (2021). https://doi.org/10.1007/s42486-021-00063-5
Kumar, P., Chauhan, S. Human activity recognition with deep learning: overview, challenges and possibilities. CCF Trans. Pervasive Comp. Interact. (2021). https://doi.org/10.1007/s42486-021-00063-5
Abstract
Human Activity Recognition (HAR) has become a vibrant research field over the last decade, especially because of the spread of electronic devices like mobile phones, smart cell phones, and video cameras in our daily lives. In addition, the progress of deep learning and other algorithms has made it possible for researchers to use HAR in many fields including sports, health, and well-being. HAR is, for example, one of the most promising resources for helping older people with the support of their cognitive and physical function through day-to-day activities. This study focuses on the key role machine learning plays in the development of HAR applications. While numerous HAR surveys and review articles have previously been carried out, the main/overall HAR issue was not taken into account, and these studies concentrate only on specific HAR topics. A detailed review paper covering major HAR topics is therefore essential. This study analyses the most up-to-date studies on HAR in recent years and provides a classification of HAR methodology and demonstrates advantages and disadvantages for each group of methods. This paper finally addresses many problems in the current HAR subject and provides recommendations for potential study.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: Pranjal Kumar
Commenter's Conflict of Interests: Author