Preprint Hypothesis Version 3 Preserved in Portico This version is not peer-reviewed

Human Activity Recognition with Deep Learning: Overview, Challenges & 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

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.

Subject Areas

Learning (artificial intelligence); Neural networks; Activity recognition

Comments (1)

Comment 1
Received: 2 June 2021
Commenter: Pranjal Kumar
Commenter's Conflict of Interests: Author
Comment: More refinement is done from the previous version.
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