Skip to main content

Industry 5.0: Towards Human Centered Design in Human Machine Interaction

  • Conference paper
  • First Online:
Research and Innovation Forum 2023 (RIIFORUM 2023)

Abstract

The Industry 5.0 paradigm presents new opportunities and challenges that companies must take advantage of to remain competitive in a highly competitive environment. The main idea is the intelligent collaboration between humans and machines, i.e., to combine human intelligence and creativity with efficient, intelligent and precise machines. The increasing digitization of manufacturing processes has required the use of artificial intelligence in manufacturing. As a result, jobs are being adapted and reconfigured. humans and machines have unique capabilities, which can be enhanced by a synergic relationship between them. In this study, a review of artificial intelligence architectures applied to the operator’s state recognition in manufacturing is proposed. The aim is to understand the human-robot interaction to achieve a complete collaboration between the robot and the human. In this way, efficiency is increased and the risks of possible accidents for the operator are reduced, prioritizing their safety. In this framework, a model based on artificial intelligence is proposed that collects data on the operator’s state and assesses potential dangerous situations while operating with the robot. The robot will be able to perform its task completely and quickly without worrying about human interaction, unless it detects that its actions involve a risk to the human worker. Possible sensors that will monitor the human state over time are evaluated and the model will collect the data and learn from the experience. The proposed conceptual model is based on three basic principles to Industry 5.0: safety, reliability, and human-centered design.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Leng, J., Sha, W., Wang, B., Zheng, P., Zhuang, C., Liu, Q., Wuest, T., Mourtzis, D., Wang, L.: Industry 5.0: prospect and retrospect. J. Manuf. Syst. 65, 279–295 (2022). https://doi.org/10.1016/j.jmsy.2022.09.017

  2. Lu, Y., Zheng, H., Chand, S., Xia, W., Liu, Z., Xu, X., Wang, L., Qin, Z., Bao, J.: Outlook on human-centric manufacturing towards Industry 5.0. J. Manuf. Syst. 62, 612–627 (2022). https://doi.org/10.1016/j.jmsy.2022.02.001

  3. Visvizi, A.: Computers and human behavior in the smart city: issues, topics, and new research directions. Comput. Hum. Behav. 140, 107596 (2022). https://doi.org/10.1016/j.chb.2022.107596

  4. Visvizi, A., Lytras, M.D., Aljohani, N.: Big data research for politics: human centric big data research for policy making, politics, governance and democracy. J. Ambient Intell. Hum. Comput. 12, 4303–4304 (2021). https://doi.org/10.1007/s12652-021-03171-3

  5. Troisi, O., Grimaldi, M.: Guest editorial: data-driven orientation and open innovation: the role of resilience in the (co-) development of social changes. Transforming Gov. People Process Policy 16(2), 165–171 (2022). https://doi.org/10.1108/TG-05-2022-317

  6. Adel, A.: Future of industry 5.0 in society: human-centric solutions, challenges and prospective research areas. J. Cloud Comput. 11(1), 40 (2022). https://doi.org/10.1186/s13677-022-00314-5

  7. Nahavandi, S.: Industry 5.0-a human-centric solution. Sustainability 11(16), 4371 (2019). https://doi.org/10.3390/su11164371

  8. Colom, J.F., Mora, H., Gil, D., Signes-Pont, M.T.: Collaborative building of behavioural models based on internet of things. Comput. Electr. Eng. 58, 385–396 (2017). https://doi.org/10.1016/j.compeleceng.2016.08.019

  9. Ferrández-Pastor, F.J., Mora-Mora, H., Sánchez-Romero, J.L., Nieto-Hidalgo, M., García-Chamizo, J.M.: Interpreting human activity from electrical consumption data using reconfigurable hardware and hidden Markov models. J. Ambient Intell. Hum. Comput. 8(4), 469–483 (2017). https://doi.org/10.1007/s12652-016-0431-y

  10. Jahanmahin, R., Masoud, S., Rickli, J., Djuric, A.: Human-robot interactions in manufacturing: a survey of human behavior modeling. Robot. Comput. Integr. Manuf. 78, 102404 (2022). https://doi.org/10.1016/j.rcim.2022.102404

  11. Visvizi, A., Troisi, O., Grimaldi, M., Loia, F.: Think human, act digital: activating data-driven orientation in innovative start-ups. Eur. J. Innov. Manage. 25(6), 452–478 (2022). https://doi.org/10.1108/EJIM-04-2021-0206

  12. Coronado, E., Kiyokawa, T., Ricardez, G.A.G., Ramirez-Alpizar, I.G., Venture, G., Yamanobe, N.: Evaluating quality in human-robot interaction: a systematic search and classification of performance and human-centered factors, measures and metrics towards an industry 5.0. J. Manuf. Syst. 63, 392–410 (2022). https://doi.org/10.1016/j.jmsy.2022.04.007

  13. Visvizi, A., Mora, H., Varela-Guzman, E.G.: The case of rwallet: a blockchain-based tool to navigate some challenges related to irregular migration. Comput. Hum. Behav. 139, 107548 (2023). https://doi.org/10.1016/j.chb.2022.107548

  14. Troisi, O., Fenza, G., Grimaldi, M., Loia, F.: Covid-19 sentiments in smart cities: the role of technology anxiety before and during the pandemic. Comput. Hum. Behav. 126, 106986 (2022). https://doi.org/10.1016/j.chb.2021.106986

  15. Nordin, N., Zainol, Z., Mohd Noor, M.H., Chan, L.F.: Suicidal behaviour prediction models using machine learning techniques: a systematic review. Artif. Intell. Med. 132, 102395 (2022). https://doi.org/10.1016/j.artmed.2022.102395

  16. Alnuaim, A.A., Zakariah, M., Alhadlaq, A., Shashidhar, C., Hatamleh, W.A., Tarazi, H., Shukla, P.K., Ratna, R.: Human-computer interaction with detection of speaker emotions using convolution neural networks. Comput. Intell. Neurosci. 2022, e7463091 (2022). https://doi.org/10.1155/2022/7463091

  17. Kashef, M., Visvizi, A., Troisi, O.: Smart city as a smart service system: human-computer interaction and smart city surveillance systems. Comput. Hum. Behav. 124, 106923 (2021). https://doi.org/10.1016/j.chb.2021.106923

  18. Lin, C.J., Lukodono, R.P.: Classification of mental workload in human-robot collaboration using machine learning based on physiological feedback. J. Manuf. Syst. 65, 673–685 (2022). https://doi.org/10.1016/j.jmsy.2022.10.017

  19. Moutinho, D., Rocha, L.F., Costa, C.M., Teixeira, L.F., Veiga, G.: Deep learning-based human action recognition to leverage context awareness in collaborative assembly. Robot. Comput. Integr. Manuf. 80, 102449 (2023). https://doi.org/10.1016/j.rcim.2022.102449

  20. Rožanec, J.M., Novalija, I., Zajec, P., Kenda, K., Tavakoli Ghinani, H., Suh, S., Veliou, E., Papamartzivanos, D., Giannetsos, T., Menesidou, S.A., Alonso, R., Cauli, N., Meloni, A., Recupero, D.R., Kyriazis, D., Sofianidis, G., Theodoropoulos, S., Fortuna, B., Mladenić, D., Soldatos, J.: Human-centric artificial intelligence architecture for industry 5.0 applications. Int. J. Prod. Res. 61(20), 6847–6872 (2022). https://doi.org/10.1080/00207543.2022.2138611

  21. Li, C., Zheng, P., Yin, Y., Wang, B., Wang, L.: Deep reinforcement learning in smart manufacturing: a review and prospects. CIRP J. Manuf. Sci. Technol. 40, 75–101 (2023). https://doi.org/10.1016/j.cirpj.2022.11.003

  22. Zhou, T., Wang, Y., Zhu, Q., Du, J.: Human hand motion prediction based on feature grouping and deep learning: pipe skid maintenance example. Autom. Constr. 138, 104232 (2022). https://doi.org/10.1016/j.autcon.2022.104232

  23. Zhang, R., Lv, Q., Li, J., Bao, J., Liu, T., Liu, S.: A reinforcement learning method for human-robot collaboration in assembly tasks. Robot. Comput. Integr. Manuf. 73, 102227 (2022). https://doi.org/10.1016/j.rcim.2021.102227

  24. Choi, S.H., Park, K.B., Roh, D.H., Lee, J.Y., Mohammed, M., Ghasemi, Y., Jeong, H.: An integrated mixed reality system for safety-aware human-robot collaboration using deep learning and digital twin generation. Robot. Comput. Integr. Manuf. 73, 102258 (2022). https://doi.org/10.1016/j.rcim.2021.102258

  25. Yin, Y., Zheng, P., Li, C., Wang, L.: A state-of-the-art survey on augmented reality-assisted digital twin for futuristic human-centric industry transformation. Robot. Comput. Integr. Manuf. 81, 102515 (2023). https://doi.org/10.1016/j.rcim.2022.102515

  26. Wang, H., Lv, L., Li, X., Li, H., Leng, J., Zhang, Y., Thomson, V., Liu, G., Wen, X., Sun, C., Luo, G.: A safety management approach for Industry 5.0’s human-centered manufacturing based on digital twin. J. Manuf. Syst. 66, 1–12 (2023). https://doi.org/10.1016/j.jmsy.2022.11.013

  27. Fan, J., Zheng, P., Li, S.: Vision-based holistic scene understanding towards proactive human-robot collaboration. Robot. Comput. Integr. Manuf. 75, 102304 (2022). https://doi.org/10.1016/j.rcim.2021.102304

  28. Sanchez-Ribes, V., Macia-Lillo, A., Mora, H., Jimeno-Morenilla, A.: Efficient GPU cloud architectures for outsourcing high-performance processing to the cloud. In: The International Journal of Advanced Manufacturing Technology, pp. 1–10 (2023). https://doi.org/10.1007/s00170-023-11252-0

  29. Nguyen, D.C., Ding, M., Pathirana, P.N., Seneviratne, A., Li, J., Poor, H.V.: Federated learning for internet of things: a comprehensive survey. IEEE Commun. Surv. Tutorials 23(3), 1622–1658 (2021). https://doi.org/10.1109/COMST.2021.3075439

  30. Zheng, Z., Zhou, Y., Sun, Y., Wang, Z., Liu, B., Li, K.: Applications of federated learning in smart cities: recent advances, taxonomy, and open challenges. Connection Sci. 34(1), 1–28 (2022). https://doi.org/10.1080/09540091.2021.1936455

  31. Jiang, J.C., Kantarci, B., Oktug, S., Soyata, T.: Federated learning in smart city sensing: challenges and opportunities. Sensors 20(21), 6230 (2020). https://doi.org/10.3390/s20216230

  32. Elouali, A., Mora, H.M., Mora-Gimeno, F.J.: Data transmission reduction formalization for cloud offloading-based IoT systems. J. Cloud Comput. 12(1), 1–12 (2023). https://doi.org/10.1186/s13677-023-00424-8

  33. Zhu, J., Cao, J., Saxena, D., Jiang, S., Ferradi, H.: Blockchain-empowered federated learning: challenges, solutions, and future directions. ACM Comput. Surv. 55(11), 1–31 (2023)

    Google Scholar 

  34. Issa, W., Moustafa, N., Turnbull, B., Sohrabi, N., Tari, Z.: Blockchain-based federated learning for securing internet of things: a comprehensive survey. ACM Comput. Surv. 55(9), 1–43 (2023). https://doi.org/10.1145/3560816

  35. Mendoza-Tello, J.C., Mora, H., Mendoza-Tello, T.: The role of blockchain for introducing resilience in insurance domain: a systematic review. In: The International Research and Innovation Forum, pp. 587–596. Springer (2022). https://doi.org/10.1007/978-3-031-19560-0_50

  36. Qu, Y., Uddin, M.P., Gan, C., Xiang, Y., Gao, L., Yearwood, J.: Blockchain-enabled federated learning: a survey. ACM Comput. Surv. 55(4), 1–35 (2022). https://doi.org/10.1145/3524104

  37. Mora, H., Mendoza-Tello, J.C., Varela-Guzman, E.G., Szymanski, J.: Blockchain technologies to address smart city and society challenges. Comput. Hum. Behav. 122, 106854 (2021). https://doi.org/10.1016/j.chb.2021.106854

  38. Lu, Y., Huang, X., Dai, Y., Maharjan, S., Zhang, Y.: Blockchain and federated learning for privacy-preserved data sharing in industrial iot. IEEE Trans. Ind. Inf. 16(6), 4177–4186 (2019). https://doi.org/10.1109/TII.2019.2942190

Download references

Acknowledgements

This work was supported by the Spanish Research Agency (AEI) under project HPC4Industry PID2020-120213RB-I00. Doi: 10.13039/501100011033

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tamai Ramírez-Gordillo .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ramírez-Gordillo, T., Mora, H., Pujol-Lopez, F.A., Jimeno-Morenilla, A., Maciá-Lillo, A. (2024). Industry 5.0: Towards Human Centered Design in Human Machine Interaction. In: Visvizi, A., Troisi, O., Corvello, V. (eds) Research and Innovation Forum 2023. RIIFORUM 2023. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-031-44721-1_50

Download citation

Publish with us

Policies and ethics