Preprint Hypothesis Version 4 Preserved in Portico This version is not peer-reviewed

Human Activity Recognition with Deep Learning: Methods, Progress & Possibilities

Pranjal Kumar * ORCID logo
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)

A peer-reviewed article of this Preprint also exists.

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

Journal reference: CCF Transactions on Pervasive Computing and Interaction 2021
DOI: 10.1007/s42486-021-00063-5

Abstract

Over the past decade, recognition of human activities (HAR) has become a vibrant field of research, in particular, the spread in our everyday lives of electronics such as mobile phones, smart cell phones, and video cameras. Furthermore, the advancement in the field of deep methodologies and other paradigms have enabled scientists to enable HAR in many areas, consisting of activities in fitness and wellness. For instance, HAR is one of many resorts to support older people through day-to-day activities to support their cognition and physicality. This study is centered on the key aspects deep learning plays in the development of HAR applications. Although numerous HAR examination studies were carried out previously, there have been no overall studies on this subject, in all the earlier studies there were only specific HAR-related subjects. A detailed review covering all the main subjects in this area is therefore essential. This study discusses the latest developments and works in HAR. It separates the methods and the advantages and disadvantages of each method group. This paper finally addresses many problems in the current HAR subject and provides recommendations for potential study.

Subject Areas

Learning (artificial intelligence); Neural networks; Activity recognition

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Comment 1
Received: 7 June 2021
Commenter: Pranjal Kumar
Commenter's Conflict of Interests: Author
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