PreprintReviewVersion 1Preserved in Portico This version is not peer-reviewed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
The growing use of sensor tools and the Internet of Things requires sensors to understand the applications. There are major
difficulties in realistic situations, though, that can impact the efficiency of the recognition system. Recently, as the utility of
deep learning in many fields has been shown, various deep approaches were researched to tackle the challenges of detection
and recognition. We present in this review a sample of specialized deep learning approaches for the identification of sensor-based human behaviour. Next, we present the multi-modal sensory data and include information for the public databases which
can be used in different challenge tasks for study. A new taxonomy is then suggested, to organize deep approaches according
to challenges. Deep problems and approaches connected to problems are summarized and evaluated to provide an analysis of
the ongoing advancement in science. By the conclusion of this research, we are answering unanswered issues and providing
perspectives into the future.
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.
