Human-Focused Digital Twin Applications for Occupational Safety and Health in Workplaces: A Brief Survey and Research Directions
Source abstract
Abstract
Occupational safety and health is among the most challenging issues in many industrial workplaces, in that various factors can cause occupational illness and injury. Robotics, automation, and other state-of-the-art technologies represent risks that can cause further injuries and accidents. However, the tools currently used to assess risks in workplaces require manual work and are highly subjective. These tools include checklists and work assessments conducted by experts. Modern Industry 4.0 technologies such as a digital twin, a computerized representation in the digital world of a physical asset in the real world, can be used to provide a safe and healthy work environment to human workers and can reduce occupational injuries and accidents. These digital twins should be designed to collect, process, and analyze data about human workers. The problem is that building a human-focused digital twin is quite challenging and requires the integration of various modern hardware and software components. This paper aims to provide a brief survey of recent research papers on digital twins, focusing on occupational safety and health applications, which is considered an emerging research area. The authors focus on enabling technologies for human data acquisition and human representation in a virtual environment, on data processing procedures, and on the objectives of such applications. Additionally, this paper discusses the limitations of existing studies and proposes future research directions.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Digital Transformation in Industry · Ergonomics and Human Factors
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Industrial and Management Systems Engineering, Dong-A University, Busan 49315, Republic of Korea
Ingram School of Engineering, Texas State University, San Marcos, TX 78666, USA
E-Learning Center, Busan University of Foreign Studies, Busan 46234, Republic of Korea
Funding
Ministry of Education of the Republic of Korea · National Research Foundation of Korea
References (66)
Show source-provided references
1. Occupational health and safety risk assessment: A systematic literature review of models, methods, and applications10.1016/j.ssci.2022.106050 ↗
2. Human factors and ergonomics in manufacturing in the industry 4.0 context—A scoping review10.1016/j.techsoc.2021.101572 ↗
3. Park, J.S., Lee, S.J., Jimenez, J., Kim, S.K., and Kim, J.W. (2020). Indoor positioning-based mobile resource movement data management system for smart factory operations management. Int. J. Distrib. Sens. Netw., 16.10.1177/1550147720909760 ↗
4. An integrated mixed reality system for safety-aware human-robot collaboration using deep learning and digital twin generation10.1016/j.rcim.2021.102258 ↗
5. Constantino, F., Falegnami, A., Fedele, L., Bernabei, M., Stabile, S., and Bentivenga, R. (2021). New and emerging hazards for health and safety within digitalized manufacturing systems. Sustainability, 13.10.3390/su131910948 ↗
6. Occupational health and safety in the industry 4.0 era: A cause for major concern?10.1016/j.ssci.2018.06.012 ↗
7. Towards dynamic safety assurance for Industry 4.010.1016/j.sysarc.2020.101914 ↗
8. Development and validation of guidelines for safety in human-robot collaborative assembly systems10.1016/j.cie.2021.107801 ↗
9. Graphics and media technologies for operators in Industry 4.010.1109/MCG.2018.053491736 ↗
10. Industry 4.0 and the human factor—A systems framework and analysis methodology for successful development10.1016/j.ijpe.2020.107992 ↗
11. A review of the roles of digital twin in CPS-based production systems10.1016/j.promfg.2017.07.198 ↗
12. Cross reality to enhance worker cognition in industrial assembly operations10.1007/s00170-019-03939-0 ↗
13. IoT and digital twin enabled smart tracking for safety management10.1016/j.cor.2020.105183 ↗
14. The digital twin implementation for linking the virtual representation of human-focused production tasks to their physical counterpart in the factory-floor10.1080/0951192X.2018.1529430 ↗
15. Manufacturing operator ergonomics: A conceptual digital twin approach to detect biochemical fatigue10.1109/ACCESS.2022.3145984 ↗
16. Bevilacqua, M., Bottani, E., Ciarapica, F.E., Constantino, F., Di Donato, L., Ferraro, A., Mazzuto, G., Monteriù, A., Nardini, G., and Ortenzi, M. (2020). Digital twin reference model development to prevent operators’ risk in process plants. Sustainability, 12.10.3390/su12031088 ↗
17. Greco, A., Caterino, M., Fera, M., and Gerbino, S. (2020). Digital twin for monitoring ergonomics during manufacturing production. Appl. Sci., 10.10.3390/app10217758 ↗
18. Ergonomic methods for assessing exposure to risk factors for work-related musculoskeletal disorders10.1093/occmed/kqi082 ↗
19. Towards a conceptual framework of OSH risk management in smart working environments based on smart PPE, ambient intelligence and the Internet of Things technologies10.1080/10803548.2016.1214431 ↗
20. Cyber-physical-system-based safety monitoring for blind hoisting with the internet of things: A case study10.1016/j.autcon.2018.10.017 ↗
21. Di Carlo, F., Mazzuto, G., Bevilacqua, M., and Ciarapica, F.E. (2021). Retrofitting a process plant in an Industry 4.0 perspective for improving safety and maintenance performance. Sustainability, 13.10.3390/su13020646 ↗
22. A survey on digital twin: Definitions, characteristics, applications, and design implications10.1109/ACCESS.2019.2953499 ↗
23. Hou, L., Wu, S., Zhang, G., Tan, Y., and Wang, X. (2020). Literature review of digital twins applications in construction workforce safety. Appl. Sci., 11.10.3390/app11010339 ↗
24. Innovative system for real-time ergonomic feedback in industrial manufacturing10.1016/j.apergo.2012.11.008 ↗
25. Innovative real-time system to integrate ergonomic evaluations into warehouse design and management10.1016/j.cie.2014.08.018 ↗
26. Ergotac: A tactile feedback interface for improving human ergonomics in workplaces10.1109/LRA.2018.2864356 ↗
27. Digital twins to enhance the integration of ergonomics in the workplace design10.1016/j.ergon.2019.02.001 ↗
28. Adaptive motion planning for a collaborative robot based on prediction uncertainty to enhance human safety and work efficiency10.1109/TRO.2019.2911800 ↗
29. A cyber physical system (CPS) approach for safe human-robot collaboration in a shared workplace10.1016/j.rcim.2018.10.003 ↗
30. The effectiveness of virtual environments in developing collaborative strategies between industrial robots and humans10.1016/j.rcim.2018.07.006 ↗
31. Cyber-physical postural training system for construction workers10.1016/j.autcon.2020.103272 ↗
32. Motion Analysis System (MAS) for production and ergonomics assessment in the manufacturing processes10.1016/j.cie.2018.10.046 ↗
33. Analyzing the kinematic and kinetic contributions of the human upper body’s joints for ergonomics assessment10.1007/s12652-020-01926-y ↗
34. Recurrent neural network for motion trajectory prediction in human-robot collaborative assembly10.1016/j.cirp.2020.04.077 ↗
35. Dimitropoulos, N., Togias, T., Zacharaki, N., Michalos, G., and Makris, S. (2021). Seamless human-robot collaborative assembly using artificial intelligence and wearable devices. Appl. Sci., 11.10.3390/app11125699 ↗
36. Cyber physical system for safety management in smart construction site10.1108/ECAM-10-2019-0578 ↗
37. Collision-free human-robot collaboration based on context awareness10.1016/j.rcim.2020.101997 ↗
38. A digital twin-driven human-robot collaborative assembly approach in the wake of COVID-1910.1016/j.jmsy.2021.02.011 ↗
39. Maruyama, T., Ueshiba, T., Tada, M., Toda, H., Endo, Y., Domae, Y., Nakabo, Y., Mori, T., and Suita, K. (2021). Digital twin-driven human robot collaboration using a digital human. Sensors, 21.10.3390/s21248266 ↗
40. WEM-Platform: A real-time platform for full-body ergonomic assessment and feedback in manufacturing and logistics systems10.1016/j.cie.2021.107881 ↗
41. A methodological framework to integrate motion capture system and virtual reality for assembly system 4.0 workplace design10.1016/j.ssci.2021.105561 ↗
42. Digital ergonomics: An evaluation framework for the ergonomic risk assessment of heterogeneous workers10.1080/0951192X.2022.2090023 ↗
43. Characterising the Digital Twin: A systematic literature review10.1016/j.cirpj.2020.02.002 ↗
44. Agnusdei, G.P., Elia, V., and Gnoni, M.G. (2021). Is digital twin technology supporting safety management? A bibliometric and systematic review. Appl. Sci., 11.10.3390/app11062767 ↗
45. Smart factory of Industry 4.0: Key technologies, application case, and challenges10.1109/ACCESS.2017.2783682 ↗
46. Jo, D.S., Kim, T.W., and Kim, J.W. (2020). Intelligent rework process management system under smart factory environment. Sustainability, 12.10.3390/su12239883 ↗
47. Interactive job sequencing system for small make-to-order manufacturers under smart manufacturing environment10.1007/s12083-019-00808-1 ↗
48. Corallo, A., Del Vecchio, V., Lezzi, M., and Morciano, P. (2021). Shop floor digital twin in smart manufacturing: A systematic literature review. Sustainability, 13.10.3390/su132312987 ↗
49. Sun, S., Zheng, X., Gong, B., Garcia Paredes, J., and Ordieres-Meré, J. (2020). Healthy operator 4.0: A human cyber-physical system architecture for smart workplaces. Sensors, 20.10.3390/s20072011 ↗
50. “Industrie 4.0” and smart manufacturing—A review of research issues and application examples10.20965/ijat.2017.p0004 ↗
51. Ruppert, T., Jaskó, S., Holczinger, T., and Abonyi, J. (2018). Enabling technologies for operator 4.0: A survey. Appl. Sci., 8.10.3390/app8091650 ↗
52. Exploring the potential of Operator 4.0 interface and monitoring10.1016/j.cie.2018.12.047 ↗
53. A support-design framework for cooperative robots systems in labor-intensive manufacturing processes10.1016/j.jmsy.2021.10.008 ↗
54. Židek, K., Piteľ, J., Balog, M., Hošovský, A., Hladký, V., Lazorík, P., Iakovets, A., and Demčák, J. (2021). CNN training using 3D virtual models for assisted assembly with mixed reality and collaborative robots. Appl. Sci., 11.10.3390/app11094269 ↗
55. Digital twin architecture and standards
56. An application framework of digital twin and its case study10.1007/s12652-018-0911-3 ↗
57. A methodology for digital twin modeling and deployment for Industry 4.010.1109/JPROC.2020.3032444 ↗
58. Židek, K., Piteľ, J., Adámek, M., Lazorík, P., and Hošovský, A. (2020). Digital twin of experimental smart manufacturing assembly system for Industry 4.0 concept. Sustainability, 12.10.3390/su12093658 ↗
59. Kong, X.T., Yang, X., Huang, G.Q., and Luo, H. (2018, January 27–29). The impact of industrial wearable system on industry 4.0. Proceedings of the 2018 IEEE 15th International Conference on Networking, Sensing and Control, Zhuhai, China.10.1109/ICNSC.2018.8361266 ↗
60. Caputo, F., Greco, A., D’Amato, E., Notaro, I., and Spada, S. (2018, January 21–25). IMU-based motion capture wearable system for ergonomic assessment in industrial environment. Proceedings of the International Conference on Applied Human Factors and Ergonomics, Orlando, FL, USA.10.1007/978-3-319-94619-1_21 ↗
61. Instrumented ergonomic risk assessment using wearable inertial measurement units: Impact of joint angle convention10.1109/ACCESS.2020.3048645 ↗
62. RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments10.1177/0278364911434148 ↗
63. Learning to estimate 3d human pose from point cloud10.1109/JSEN.2020.2999849 ↗
64. Comparison of OWAS, RULA and REBA for assessing potential work-related musculoskeletal disorders10.1016/j.ergon.2021.103140 ↗
65. Applying reason: The human factors analysis and classification system (HFACS)
66. People or systems? To blame is human. The fix is to engineer