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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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
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
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
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
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
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
Nahavandi, S.: Industry 5.0-a human-centric solution. Sustainability 11(16), 4371 (2019). https://doi.org/10.3390/su11164371
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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)
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
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
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
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
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
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
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
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
DOI: https://doi.org/10.1007/978-3-031-44721-1_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-44720-4
Online ISBN: 978-3-031-44721-1
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)