Human-centric digital twins in industry: a comprehensive review of enabling technologies and implementation strategies

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Asad, U, Khan, M, Khalid, A ORCID: https://orcid.org/0000-0001-5270-6599 and Lughmani, WA, 2023. Human-centric digital twins in industry: a comprehensive review of enabling technologies and implementation strategies. Sensors, 23 (8): 3938. ISSN 1424-8220

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Official URL: https://doi.org/10.3390/s23083938

Abstract

The last decade saw the emergence of highly autonomous, flexible, re-configurable Cyber-Physical Systems. Research in this domain has been enhanced by the use of high-fidelity simulations, including Digital Twins, which are virtual representations connected to real assets. Digital Twins have been used for process supervision, prediction, or interaction with physical assets. Interaction with Digital Twins is enhanced by Virtual Reality and Augmented Reality, and Industry 5.0-focused research is evolving with the involvement of the human aspect in Digital Twins. This paper aims to review recent research on Human-Centric Digital Twins (HCDTs) and their enabling technologies. A systematic literature review is performed using the VOSviewer keyword mapping technique. Current technologies such as motion sensors, biological sensors, computational intelligence, simulation, and visualization tools are studied for the development of HCDTs in promising application areas. Domain-specific frameworks and guidelines are formed for different HCDT applications that highlight the workflow and desired outcomes, such as the training of AI models, the optimization of ergonomics, the security policy, task allocation, etc. A guideline and comparative analysis for the effective development of HCDTs are created based on the criteria of Machine Learning requirements, sensors, interfaces, and Human Digital Twin inputs.

Item Type:	Journal article
Publication Title:	Sensors
Creators:	Asad, U., Khan, M., Khalid, A. and Lughmani, W.A.
Publisher:	MDPI
Date:	12 April 2023
Volume:	23
Number:	8
ISSN:	1424-8220
Identifiers:	Number Type 10.3390/s23083938 DOI 1750392 Other
Rights:	© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Divisions:	Schools > School of Science and Technology
Record created by:	Laura Ward
Date Added:	17 Apr 2023 09:13
Last Modified:	17 Apr 2023 09:13
URI:	https://irep.ntu.ac.uk/id/eprint/48751