Abstract
The integration of Artificial Intelligence (AI) into ergonomics represents a transformative paradigm in the optimization of workplace environments. This report explores the multifaceted impact of AI in ergonomics, emphasizing its significance in enhancing both worker well-being and operational efficiency. AI-driven solutions in ergonomics leverage advanced data analytics to assess human factors, task demands, and environmental conditions, offering a data-driven approach to ergonomic design. This not only minimizes the risk of workplace injuries but also fosters environments that promote employee health and productivity. The research further delves into the study that employs various citation and data analysis tools to conduct comprehensive research. During the initial data collection phase, Google Ngram was employed to anticipate trends in topics. Additionally, MAXQDA, VOSviewer, BibExcel, Vos viewer and Citespace were utilized for the systematic analysis of citations and associated data. Metadata extraction was carried out utilizing the Purdue Library database, encompassing Scopus, Web of Science, Harzing’s Publish or Perish, and Google Scholar. Following several bibliometric analyses, it became evident that artificial intelligence (AI) significantly influences the fields of ergonomics and safety. The synergy between AI and ergonomics is positioned as a cornerstone in the evolution of Industrial Engineering, empowering professionals to design systems that prioritize safety while embracing technological advancements. The societal response to this burgeoning field necessitates strategic funding for research and education, fostering interdisciplinary collaboration and ensuring a skilled workforce. In essence, this abstract underscore the transformative potential of AI in ergonomics, offering a forward-looking perspective on the integration of technology to create safer, more efficient, and ethically grounded workplaces.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Donisi, L., Cesarelli, G., Pisani, N., Ponsiglione, A.M., Ricciardi, C., Capodaglio, E.: Wearable sensors and artificial intelligence for physical ergonomics: a systematic review of literature. Diagnostics 12(12), 3048 (2022). https://doi.org/10.3390/diagnostics12123048
Sawyer, B.D., Miller, D.B., Canham, M., Karwowski, W.: Human factors and ergonomics in design of a 3: automation, autonomy, and artificial intelligence. In: Handbook of Human Factors and Ergonomics, pp. 1385–1416. Wiley (2021). https://doi.org/10.1002/9781119636113.ch52
Hamilton, B.C., et al.: Artificial intelligence based real-time video ergonomic assessment and training improves resident ergonomics. Am. J. Surg. 226(5), 741–746 (2023). https://doi.org/10.1016/j.amjsurg.2023.07.028
Kistan, T., Gardi, A., Sabatini, R.: Machine learning and cognitive ergonomics in air traffic management: recent developments and considerations for certification. Aerospace 5(4), 103 (2018). https://doi.org/10.3390/aerospace5040103
Rychtyckyj, N.: Ergonomics analysis for vehicle assembly using artificial intelligence. AI Mag. 26(3), 41–50 (2005). https://doi.org/10.1609/aimag.v26i3.1824
Xu, W., Furie, D., Mahabhaleshwar, M., Suresh, B., Chouhan, H.: Applications of an interaction, process, integration and intelligence (IPII) design approach for ergonomics solutions. Ergonomics 62(7), 954–980 (2019). https://doi.org/10.1080/00140139.2019.1588996
Hästbacka, D., Mätäsniemi, T.: Unifying process design with automation and control application development - an approach based on information integration and model-driven methods. IFAC Proc. Vol. 42(4), 1227–1232 (2009). https://doi.org/10.3182/20090603-3-RU-2001.0468
Grote, G.: Shaping the development and use of artificial intelligence: how human factors and ergonomics expertise can become more pertinent. Ergonomics 66, 1–9 (2023). https://doi.org/10.1080/00140139.2023.2278408
Low, J.X., Wei, Y., Chow, J., Ali, I.F.B.: ActSen - AI-enabled real-time IoT-based ergonomic risk assessment system. In: 2019 IEEE International Congress on Internet of Things (ICIOT), pp. 76–78 (2019). https://doi.org/10.1109/ICIOT.2019.00024
Lau, N., Hildebrandt, M., Jeon, M.: Ergonomics in AI: designing and interacting with machine learning and AI. Ergon. Des. 28(3), 3–3 (2020). https://doi.org/10.1177/1064804620915238
Aubin, F., Prevost, M.-C.: Using prospective ergonomics to identify opportunities from recent technological advances in AI: the case of a west African Bank. In: Bagnara, S., Tartaglia, R., Albolino, S., Alexander, T., Fujita, Y. (eds.) IEA 2018. AISC, vol. 824, pp. 1365–1371. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-96071-5_138
Salmon, P.M., et al.: Managing the risks of artificial general intelligence: a human factors and ergonomics perspective. Hum. Factors Ergon. Manuf. Serv. Ind. 33(5), 366–378 (2023). https://doi.org/10.1002/hfm.20996
Fazelnia, M., Okutan, A., Mirakhorli, M.: Supporting artificial intelligence/machine learning security workers through an adversarial techniques, tools, and common knowledge framework. IEEE Secur. Priv. 21(1), 37–48 (2022). https://doi.org/10.1109/MSEC.2022.3221058
Lee, S., Liu, L., Radwin, R., Li, J.: Machine learning in manufacturing ergonomics: recent advances, challenges, and opportunities. IEEE Robot. Autom. Lett. 6(3), 5745–5752 (2021). https://doi.org/10.1109/LRA.2021.3084881
Chiang, L., Reis, M., Shuang, B., Jiang, B., Valleau, S.: Editorial: recent advances of AI and machine learning methods in integrated R&D, manufacturing, and supply chain. Front. Chem. Eng. 4, 1056122 (2022). https://doi.org/10.3389/fceng.2022.1056122
Lee, W.I., Shih, B.Y., Chen, C.Y.: Retraction: a hybrid artificial intelligence sales-forecasting system in the convenience store industry. Hum. Factors Ergon. Manuf. Serv. Ind. 22, 188–196 (2012). https://doi.org/10.1002/hfm.20272. Human Factors and Ergonomics in Manufacturing & Service Industries 26(2), 285–285 (2016). https://doi.org/10.1002/hfm.20651
Petrat, D.: Artificial intelligence in human factors and ergonomics: an overview of the current state of research. Discov. Artif. Intell. 1(1) (2021). https://doi.org/10.1007/s44163-021-00001-5
Lind, C.M., Abtahi, F., Forsman, M.: Wearable motion capture devices for the prevention of work-related musculoskeletal disorders in ergonomics-an overview of current applications, challenges, and future opportunities. Sensors 23(9), 4259 (2023). https://doi.org/10.3390/s23094259
Han, J., Jin Hyun, D., Jung, K., Yoon Kim, K., Youn, S.: Ergonomic design strategy for crutches of a lower-limb exoskeleton for paraplegic individuals: an experimental study. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 62(1), 1012–1016 (2018). https://doi.org/10.1177/1541931218621233
Duchon, J.C.: Evaluation of two work schedules in a mining operation. In: Aghazadeh, F. (ed.) Trends in Ergonomics/Human Factors V, pp. 151–160. North-Holland, Amsterdam (1988). Applied Ergonomics 21(1), 82–82 (1990). https://doi.org/10.1016/0003-6870(90)90118-H
Choung, H., David, P., Ross, A.: Trust in AI and its role in the acceptance of AI technologies. Int. J. Hum.-Comput. Interact. 39(9), 1727–1739 (2023). https://doi.org/10.1080/10447318.2022.2050543
Le Guillou, M., Prévot, L., Berberian, B.: Bringing together ergonomic concepts and cognitive mechanisms for human-AI agents cooperation. Int. J. Hum.-Comput. Interact. 39(9), 1827–1840 (2023). https://doi.org/10.1080/10447318.2022.2129741
Sheikh, H., Prins, C., Schrijvers, E.: Artificial intelligence: definition and background. In: Sheikh, H., Prins, C., Schrijvers, E. (eds.) Mission AI. Research for Policy (RP), pp. 15–41. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-21448-6_2
Drury, C.G.: Human factors/ergonomics implications of big data analytics: chartered institute of ergonomics and human factors annual lecture. Ergonomics 58(5), 1–15 (2015). https://www.researchgate.net/publication/274643362_Human_factorsergonomics_implications_of_big_data_analytics_Chartered_Institute_of_Ergonomics_and_Human_Factors_annual_lecture
Kanade, S.G., Duffy, V.G.: Exploring the effectiveness of virtual reality as a learning tool in the context of task interruption: a systematic review. Int. J. Ind. Ergon. 99, 103548 (2024). https://doi.org/10.1016/j.ergon.2024.103548
Kanade, S.G., Duffy, V.G.: A systematic literature review of game-based learning and safety management. In: Duffy, V.G. (ed.) HCII 2020. LNCS, vol. 12199, pp. 365–377. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49907-5_26
Kanade, S.G., Duffy, V.G.: Use of virtual reality for safety training: a systematic review. In: Duffy, V.G. (eds.) HCII 2022. LNCS, vol. 13320, pp. 364–375. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-06018-2_25
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
Somaraju, P., Kulkarni, S.S., Duffy, V.G., Kanade, S. (2024). Artificial Intelligence and Mobile Computing: Role of AI in Ergonomics. In: Duffy, V.G. (eds) Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. HCII 2024. Lecture Notes in Computer Science, vol 14711. Springer, Cham. https://doi.org/10.1007/978-3-031-61066-0_16
Download citation
DOI: https://doi.org/10.1007/978-3-031-61066-0_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-61065-3
Online ISBN: 978-3-031-61066-0
eBook Packages: Computer ScienceComputer Science (R0)