Data-Driven Human Factors Enabled Digital Twin

Mikhail V. Kolesnikov; Udayanto Dwi Atmojo; Valeriy Vyatkin

doi:10.1109/IECON51785.2023.10311802

Data-Driven Human Factors Enabled Digital Twin

Mikhail V. Kolesnikov, Udayanto Dwi Atmojo, Valeriy Vyatkin

Luleå University of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

4 Citations (Scopus)

5 Downloads (Pure)

Abstract

This paper presents a methodology for increasing human-centric production systems flexibility using human factors-enabled digital twins. The paper includes an analysis of the relevant projects that incorporate human-related data collection and processing. The proposed system is capable of collecting human factors-related data from various sources and then use a decision-making algorithm to schedule the tasks according to assessed human operator conditions in real-time. The formed Digital Twin is able to depict the condition of the labourer and production system status in real-time using Visual Components simulation environment. Shown results prove that existing production systems are capable of adapting to the changing condition of the worker flexibly, optimising workflow, distributing tasks with AGVs and cobots, and applying changes in workplace ergonomics to achieve better safety and performance of the worker.

Original language	English
Title of host publication	IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society
Publisher	IEEE
Number of pages	6
ISBN (Electronic)	979-8-3503-3182-0
DOIs	https://doi.org/10.1109/IECON51785.2023.10311802
Publication status	Published - 2023
MoE publication type	A4 Conference publication
Event	Annual Conference of the IEEE Industrial Electronics Society - Singapore, Singapore, Singapore, Singapore Duration: 16 Oct 2023 → 19 Oct 2023 Conference number: 49

Publication series

Name	IECON Proceedings (Industrial Electronics Conference)
ISSN (Print)	2162-4704
ISSN (Electronic)	2577-1647

Conference

Conference	Annual Conference of the IEEE Industrial Electronics Society
Abbreviated title	IECON
Country/Territory	Singapore
City	Singapore
Period	16/10/2023 → 19/10/2023

Keywords

Digital Twin
Human-centric production
Industry 5.0

Access to Document

10.1109/IECON51785.2023.10311802

_IECON_2023__Data_Driven_Human_Factors_Enabled_Digital_TwinAccepted author manuscript, 1.05 MBLicence: CC BY

Cite this

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title = "Data-Driven Human Factors Enabled Digital Twin",

abstract = "This paper presents a methodology for increasing human-centric production systems flexibility using human factors-enabled digital twins. The paper includes an analysis of the relevant projects that incorporate human-related data collection and processing. The proposed system is capable of collecting human factors-related data from various sources and then use a decision-making algorithm to schedule the tasks according to assessed human operator conditions in real-time. The formed Digital Twin is able to depict the condition of the labourer and production system status in real-time using Visual Components simulation environment. Shown results prove that existing production systems are capable of adapting to the changing condition of the worker flexibly, optimising workflow, distributing tasks with AGVs and cobots, and applying changes in workplace ergonomics to achieve better safety and performance of the worker.",

keywords = "Digital Twin, Human-centric production, Industry 5.0",

author = "Kolesnikov, {Mikhail V.} and Atmojo, {Udayanto Dwi} and Valeriy Vyatkin",

note = "Funding Information: This paper is supported by European Union{\textquoteright}s Horizon Europe research and innovation programme under grant agreement no. 101057083 (Project Zero-SWARM), and ”Self-Made: Self-management and device digitalization in manufacturing” project that received funding from the European Institute of Innovation and Technology Manufacturing (EIT-M); Publisher Copyright: {\textcopyright} 2023 IEEE. | openaire: EC/HE/101057083/EU//Zero-SWARM; Annual Conference of the IEEE Industrial Electronics Society, IECON ; Conference date: 16-10-2023 Through 19-10-2023",

year = "2023",

doi = "10.1109/IECON51785.2023.10311802",

language = "English",

series = "IECON Proceedings (Industrial Electronics Conference)",

publisher = "IEEE",

booktitle = "IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society",

address = "United States",

}

Kolesnikov, MV , Atmojo, UD & Vyatkin, V 2023, Data-Driven Human Factors Enabled Digital Twin. in IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society. IECON Proceedings (Industrial Electronics Conference), IEEE, Annual Conference of the IEEE Industrial Electronics Society, Singapore, Singapore, 16/10/2023. https://doi.org/10.1109/IECON51785.2023.10311802

Data-Driven Human Factors Enabled Digital Twin. / Kolesnikov, Mikhail V.; Atmojo, Udayanto Dwi ; Vyatkin, Valeriy.
IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2023. (IECON Proceedings (Industrial Electronics Conference)).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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AU - Vyatkin, Valeriy

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PY - 2023

Y1 - 2023

N2 - This paper presents a methodology for increasing human-centric production systems flexibility using human factors-enabled digital twins. The paper includes an analysis of the relevant projects that incorporate human-related data collection and processing. The proposed system is capable of collecting human factors-related data from various sources and then use a decision-making algorithm to schedule the tasks according to assessed human operator conditions in real-time. The formed Digital Twin is able to depict the condition of the labourer and production system status in real-time using Visual Components simulation environment. Shown results prove that existing production systems are capable of adapting to the changing condition of the worker flexibly, optimising workflow, distributing tasks with AGVs and cobots, and applying changes in workplace ergonomics to achieve better safety and performance of the worker.

AB - This paper presents a methodology for increasing human-centric production systems flexibility using human factors-enabled digital twins. The paper includes an analysis of the relevant projects that incorporate human-related data collection and processing. The proposed system is capable of collecting human factors-related data from various sources and then use a decision-making algorithm to schedule the tasks according to assessed human operator conditions in real-time. The formed Digital Twin is able to depict the condition of the labourer and production system status in real-time using Visual Components simulation environment. Shown results prove that existing production systems are capable of adapting to the changing condition of the worker flexibly, optimising workflow, distributing tasks with AGVs and cobots, and applying changes in workplace ergonomics to achieve better safety and performance of the worker.

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KW - Human-centric production

KW - Industry 5.0

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M3 - Conference article in proceedings

AN - SCOPUS:85179520488

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T2 - Annual Conference of the IEEE Industrial Electronics Society

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ER -

Data-Driven Human Factors Enabled Digital Twin

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

-: ZERO-SWARM/T41010

Cite this

Data-Driven Human Factors Enabled Digital Twin

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Projects

-: ZERO-SWARM/T41010

Cite this