Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed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

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.

Keywords

Learning (artificial intelligence); Neural networks; Activity recognition; Multimodal sensors

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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