Vol. 35 (2025): Volumen 35
Artículos de Investigación

Revealing workload insights in virtual assembly processes: a case study

PDF
  • Sharon,
  • Hugo I. Medellin-Castillo,
  • Eduardo Martínez-Mendoza,
  • Marina De La Vega
Sharon
Universidad Autónoma de San Luis Potosí
Hugo I. Medellin-Castillo
Universidad Autónoma de San Luis Potosí
Eduardo Martínez-Mendoza
Universidad del Istmo. Campus Tehuantepec
Marina De La Vega
Universidad Autónoma de Baja California
DOI: https://doi.org/10.15174/au.2025.4400

Published 2025-12-10

How to Cite

Macias-Velasquez, S., Medellin-Castillo, H. I., Martínez-Mendoza, E., & De La Vega, M. . (2025). Revealing workload insights in virtual assembly processes: a case study. Acta Universitaria, 35, 1–14. https://doi.org/10.15174/au.2025.4400

Copyright (c) 2025 Sharon Macias-Velasquez, Hugo I. Medellin-Castillo, Eduardo Martínez-Mendoza, Marina De La Vega

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

The use of virtual reality in industry to improve processes continues to expand, but it may also increase workload and negatively impact the well being of users.

This research aims to assess the level of workload caused by industrial assembly training in a haptic-enabled virtual reality system. Thirty inexperienced participants assembled a moderately difficult component consisting of eight pieces and were subsequently evaluated using the NASA Task Load Index. The results indicate that effort, mental demand, and frustration recorded the highest scores, while physical demand received the lowest score. Over 80% of participants experienced workload at high and very high levels, suggesting the need for immediate improvements in the training methodology.

PDF

References

  1. Andersen, B. J. H., Davis, A. T. A., Weber, G., & Wünsche, B. C. (2019). Immersion or diversion: does virtual reality make data visualisation more effective?. International Conference on Electronics, Information, and Communication (ICEIC), 1-7. https://doi.org/10.23919/ELINFOCOM.2019.8706403
  2. Bjørn, P., Han, M. L., Parezanovic, A., & Larsen, P. (2024). Social fidelity in cooperative virtual reality maritime training. Human–Computer Interaction, 1-25. https://doi.org/10.1080/07370024.2024.2372716
  3. Brunzini, A., Grandi, F., Peruzzini, M., & Pellicciari, M. (2021). Virtual training for assembly tasks: a framework for the analysis of the cognitive impact on operators. Procedia Manufacturing, 55, 527-534. https://doi.org/10.1016/j.promfg.2021.10.072
  4. Chessa, M., Maiello, G., Borsari, A., & Bex, P. J. (2019). The perceptual quality of the oculus rift for immersive virtual reality. Human–Computer Interaction, 34(1), 51-82. https://doi.org/10.1080/07370024.2016.1243478
  5. Chihara, T., Kobayashi, F., & Sakamoto, J. (2020). Evaluation of mental workload during automobile driving using one-class support vector machine with eye movement data. Applied Ergonomics, 89, 103201. https://doi.org/10.1016/J.APERGO.2020.103201
  6. Choi, S., Jung, K., & Noh, S. D. (2015). Virtual reality applications in manufacturing industries: past research, present findings, and future directions. Concurrent Engineering, 23(1), 40–63. https://doi.org/10.1177/1063293X14568814
  7. Cooper, N., Millela, F., Cant, I., White, M. D., & Meyer, G. (2021). Transfer of training—Virtual reality training with augmented multisensory cues improves user experience during training and task performance in the real world. PLoS One, 16(22), e0248225. https://doi.org/10.1371/JOURNAL.PONE.0248225
  8. De Arquer, I., & Nogareda, C. (2010). Estimación de la carga mental de trabajo: El método NASA TLX. Instituto Nacional de Seguridad e Higiene en el Trabajo. https://www.insst.es/documents/94886/327064/ntp_544.pdf/0da348cc-7006-4a8a-9cee-25ed6f59efdd
  9. Gallegos-Nieto, E., Medellín-Castillo, H. I., González-Badillo, G., Lim, T., & Ritchie, J. (2017). The analysis and evaluation of the influence of haptic-enabled virtual assembly training on real assembly performance. International Journal of Advanced Manufacturing Technology, 89, 581-598. https://doi.org/10.1007/s00170-016-9120-4
  10. Gallegos-Nieto, E., Medellin-Castillo, H. I., Xiu-Tian, Y., & Corney, J. (2020). Haptic-enabled virtual planning and assessment of product assembly. Assembly Automation, 40(4), 641-654. https://doi.org/10.1108/AA-10-2019-0169
  11. Harris, D., Wilson, M., & Vine, S. (2020). Development and validation of a simulation workload measure: the simulation task load index (SIM-TLX). Virtual Reality, 24(4), 557-566. https://doi.org/10.1007/s10055-019-00422-9
  12. Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX (Task Load Index): results of empirical and theoretical research. Advances in Psychology, 52, 139-183. https://doi.org/10.1016/S0166-4115(08)62386-9
  13. Huegel, J. C., & O’Malley, M. K. (2014). Workload and performance analyses with haptic and visually guided training in a dynamic motor skill task. In M. Garbey, B. Bass, S. Berceli, C. Collet & P. Cerveri (eds.), Computational Surgery and Dual Training: Computing, Robotics and Imaging (pp. 377-394). Springer. https://doi.org/10.1007/978-1-4614-8648-0_25
  14. Kim, M., Jeon, C., & Kim, J. (2017). A study on immersion and presence of a portable hand haptic system for immersive virtual reality. Sensors, 17(5), 1141. https://doi.org/10.3390/s17051141
  15. Kim, Y. M., Rhiu, I., & Yun, M. H. (2020). A systematic review of a virtual reality system from the perspective of user experience. International Journal of Human-Computer Interaction, 36(10), 893–910. https://doi.org/10.1080/10447318.2019.1699746
  16. Kosch, T., Karolus, J., Zagermann, J., Reiterer, H., Schmidt, A., & Woźniak, P. W. (2023). A survey on measuring cognitive workload in human-computer interaction. ACM Computing Surveys, 55(13), 1-39. https://doi.org/10.1145/3582272
  17. Kung, C. H., Hsieh, T. C., & Smith, S. (2021). Usability study of multiple vibrotactile feedback stimuli in an entire virtual keyboard input. Applied Ergonomics, 90, 103270. https://doi.org/10.1016/j.apergo.2020.103270
  18. Lawson, G., Salanitri, D., & Waterfield, B. (2016). Future directions for the development of virtual reality within an automotive manufacturer. Applied Ergonomics, 53, 323–330. https://doi.org/10.1016/j.apergo.2015.06.024
  19. Li, K., Cheng, J., Zhang, Q., & Liu, J. (2018). Hand gesture tracking and recognition based human-computer interaction system and its applications. 2018 IEEE International Conference on Information and Automation (ICIA), 667-672. https://doi.org/10.1109/ICInfA.2018.8812508
  20. MacKenzie, I. S. (2013). Human-computer interaction: An empirical research perspective. Elsevier.
  21. Marucci, M., Di Flumeri, G., Borghini, G., Sciaraffa, N., Scandola, M., Pavone, E. F., Babiloni, F., Betti, V., & Aricò, P. (2021). The impact of multisensory integration and perceptual load in virtual reality settings on performance, workload and presence. Scientific Reports, 11(1), 1-15. https://doi.org/10.1038/s41598-021-84196-8
  22. National Aeronautics and Space Administration (NASA). (2019). NASA TLX TASK LOAD INDEX. https://humansystems.arc.nasa.gov/groups/tlx/tlxpaperpencil.php
  23. Noyes, J. M., & Bruneau, D. P. J. (2007). A self-analysis of the NASA-TLX workload measure. Ergonomics, 50(4), 514-519. https://doi.org/10.1080/00140130701235232
  24. Rivera-Flor, H., Hernandez-Ossa, K. A., Longo, B., & Bastos, T. (2019). Evaluation of task workload and intrinsic motivation in a virtual reality simulator of electric-powered wheelchairs. Procedia Computer Science, 160, 641-646. https://doi.org/10.1016/j.procs.2019.11.034
  25. Sugarindra, M., Suryoputro, M. R., & Permana, A. I. (2017). Mental workload measurement in operator control room using NASA-TLX. IOP Conference Series: Materials Science and Engineering, 277, 012022. https://doi.org/10.1088/1757-899X/277/1/012022
  26. Tao, D., Tan, H., Wang, H., Zhang, X., Qu, X., & Zhang, T. (2019). A systematic review of physiological measures of mental workload. International Journal of Environmental Research and Public Health, 16(15), 2716. https://doi.org/10.3390/ijerph16152716
  27. Tcha-Tokey, K., Christmann, O., Loup-Escande, E., Loup, G., & Richir, S. (2018). Towards a model of user experience in immersive virtual environments. Advances in Human-Computer Interaction, 2018(1), 7827286. https://doi.org/10.1155/2018/7827286
  28. Tong, Q., Wei, W., Zhang, Y., Xiao, J., & Wang, D. (2023). Survey on hand-based haptic interaction for virtual reality. IEEE Transactions on Haptics, 16(2), 154-170. https://doi.org/10.1109/TOH.2023.3266199
  29. Vanneste, P., Huang, Y., Park, J. Y., Cornillie, F., Decloedt, B., & Van Den Noortgate, W. (2020). Cognitive support for assembly operations by means of augmented reality: an exploratory study. International Journal of Human-Computer Studies, 143, 102480. https://doi.org/10.1016/j.ijhcs.2020.102480
  30. World Medical Association. (2017). Declaration of Helsinki – Ethical Principles for Medical Research Involving Human Subjects. https://www.wma.net/es/policies-post/declaracion-de-helsinki-de-la-amm-principios-eticos-para-las-investigaciones-medicas-en-seres-humanos/
  31. Xia, P., Mendes, A., & Restivo, M. T. (2013). A review of virtual reality and haptics for product assembly: from rigid parts to soft cables. Assembly Automation, 33, 157–164. https://doi.org/10.1108/01445151311306672