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Industrial wearable system: the human-centric empowering technology in Industry 4.0

Author

Listed:
  • Xiang T. R. Kong

    (Shenzhen University
    The University of Hong Kong)

  • Hao Luo

    (Shenzhen University)

  • George Q. Huang

    (The University of Hong Kong)

  • Xuan Yang

    (Shenzhen University)

Abstract

The Industry 4.0 program and corresponding international initiatives continue to transform the industrial workforce and their work. The service-oriented, customer-centric and demand-driven production is pushing forward the progress of industrial automation. Even though, it does not mean that human can be fully replaced by machines/robots. There is an increasing awareness that human presence is not only one type of manufacturing capability, but also contributes to the overall system’s fault tolerant. How to achieve the seamless integration between human and machines/robots and harness human’s full potential is a critical issue for the success of Industry 4.0. In this research, a human-centric empowering technology: industrial wearable system is proposed. The aim of this system is to establish a human–cyber–physical symbiosis to support real time, trusting, and dynamic interaction among operators, machines and production systems. In order to design a substantial framework, three world-leading R&D groups in this field are investigated. Five design considerations have been identified from real-life pilot projects. The future trends and research opportunities also show great promise of industrial wearable system in the next generation of manufacturing.

Suggested Citation

  • Xiang T. R. Kong & Hao Luo & George Q. Huang & Xuan Yang, 2019. "Industrial wearable system: the human-centric empowering technology in Industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2853-2869, December.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:8:d:10.1007_s10845-018-1416-9
    DOI: 10.1007/s10845-018-1416-9
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    References listed on IDEAS

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    1. Yongxin Liao & Fernando Deschamps & Eduardo de Freitas Rocha Loures & Luiz Felipe Pierin Ramos, 2017. "Past, present and future of Industry 4.0 - a systematic literature review and research agenda proposal," International Journal of Production Research, Taylor & Francis Journals, vol. 55(12), pages 3609-3629, June.
    2. Rajagopal, 2014. "The Human Factors," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249, Palgrave Macmillan.
    3. Edna Pasher & Zvika Popper & Hadas Raz & Michael Lawo, 2010. "WearIT@work: a wearable computing solution for knowledge-based development," International Journal of Knowledge-Based Development, Inderscience Enterprises Ltd, vol. 1(4), pages 346-360.
    4. Mingzhou Liu & Jing Ma & Ling Lin & Maogen Ge & Qiang Wang & Conghu Liu, 2017. "Intelligent assembly system for mechanical products and key technology based on internet of things," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 271-299, February.
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    Cited by:

    1. Mingxing Li & Ray Y. Zhong & Ting Qu & George Q. Huang, 2022. "Spatial–temporal out-of-order execution for advanced planning and scheduling in cyber-physical factories," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1355-1372, June.
    2. Md. Al-Amin & Ruwen Qin & Md Moniruzzaman & Zhaozheng Yin & Wenjin Tao & Ming C. Leu, 2023. "An individualized system of skeletal data-based CNN classifiers for action recognition in manufacturing assembly," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 633-649, February.
    3. Simon Micheler & Yee Mey Goh & Niels Lohse, 2021. "A transformation of human operation approach to inform system design for automation," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 201-220, January.
    4. Wei Qin & Qing Hu & Zilong Zhuang & Haozhe Huang & Xiaodan Zhu & Lin Han, 2023. "IPPE-PCR: a novel 6D pose estimation method based on point cloud repair for texture-less and occluded industrial parts," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2797-2807, August.
    5. Monika Klein & Ewelina Gutowska, 2022. "The Role of Restorative Design in the Achieving Principles of Industry 5.0," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 207-214.
    6. Marina Crnjac Zizic & Marko Mladineo & Nikola Gjeldum & Luka Celent, 2022. "From Industry 4.0 towards Industry 5.0: A Review and Analysis of Paradigm Shift for the People, Organization and Technology," Energies, MDPI, vol. 15(14), pages 1-20, July.
    7. Chiara Cimini & David Romero & Roberto Pinto & Sergio Cavalieri, 2023. "Task Classification Framework and Job-Task Analysis Method for Understanding the Impact of Smart and Digital Technologies on the Operators 4.0 Job Profiles," Sustainability, MDPI, vol. 15(5), pages 1-28, February.
    8. Neeraj Gupta & Saurabh Gupta & Mahdi Khosravy & Nilanjan Dey & Nisheeth Joshi & Rubén González Crespo & Nilesh Patel, 2021. "RETRACTED ARTICLE: Economic IoT strategy: the future technology for health monitoring and diagnostic of agriculture vehicles," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1117-1128, April.
    9. Francesca Serravalle & Milena Viassone & Giacomo Chiappa, 2022. "Sensory disclosure in an augmented environment: memory of touch and willingness to buy," Italian Journal of Marketing, Springer, vol. 2022(4), pages 401-417, December.
    10. Ibrahim Yitmen & Amjad Almusaed & Sepehr Alizadehsalehi, 2023. "Investigating the Causal Relationships among Enablers of the Construction 5.0 Paradigm: Integration of Operator 5.0 and Society 5.0 with Human-Centricity, Sustainability, and Resilience," Sustainability, MDPI, vol. 15(11), pages 1-25, June.
    11. Hien Nguyen Ngoc & Ganix Lasa & Ion Iriarte, 2022. "Human-centred design in industry 4.0: case study review and opportunities for future research," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 35-76, January.
    12. Liang Hou & Roger J. Jiao, 2020. "Data-informed inverse design by product usage information: a review, framework and outlook," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 529-552, March.

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