Contained in:
Book Chapter

Cognitive Dynamics for Construction Management Learning Tasks in Mixed Reality Environments

  • Xuanchang Liu
  • Ivan Mutis

Technologies to communicate construction project information (engineering designs, schedules) have evolved into a wider range of innovative ecosystems for engineering practices (e.g., cloud-based 3D representations and advanced immersive environments). There is a lack of exploration of effective user interaction for learning and training in relation to how presented information influences cognition in these ecosystems. The presented research investigates the users’ cognitive and attentional differences using the interactive capabilities of Mixed reality (MX) technology. The enhanced user-situation interactions are analyzed by measuring cognitive dynamics with an emphasis on two processes (attentional focus and cognitive load) in relation to the challenge of the engineering learning task— defined by its complexity (limited time frame for observations of the situations, number of required observations) and nature (episodic). Cognitive dynamics were measured using an electroencephalography (EEG) device that senses electrical activity in response to changing levels of cognitive stimuli via electrodes placed on the scalp. Measuring fluctuations in cognitive processing (related to the intensity of various task demands) allows associating efforts on semantic information processing for learning and training tasks (e.g., walkthroughs for safety checks in job site in MX). The approach enhances opportunities to design technology that best adapts to the user needs for engineering practices with an efficient comprehensive performance assessment

  • Keywords:
  • Electroencephalography (EEG),
  • Dynamics of attention,
  • Cognitive load,
  • Cognitive processing,
+ Show More

Xuanchang Liu

Illinois Institute of Technology, United States - ORCID: 0009-0000-7236-5322

Ivan Mutis

Illinois Institute of Technology, United States - ORCID: 0000-0003-2707-2701

  1. Asish, S. M., Kulshreshth, A. K., & Borst, C. W. (2022). Detecting distracted students in educational VR environments using machine learning on eye gaze data. Computers & Graphics, 109, 75-87. DOI: 10.1016/J.CAG.2022.10.007
  2. Borghini, G., Vecchiato, G., Toppi, J., Astolfi, L., Maglione, A., Isabella, R., Caltagirone, C., Kong, W., Wei, D., Zhou, Z., Polidori, L., Vitiello, S., & Babiloni, F. (2012). Assessment of mental fatigue during car driving by using high resolution EEG activity and neurophysiologic indices. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2012, 6442-6445. DOI: 10.1109/EMBC.2012.6347469
  3. Chen, J., Song, X., & Lin, Z. (2016). Revealing the “Invisible Gorilla” in construction: Estimating construction safety through mental workload assessment. Automation in Construction, 63, 173-183. DOI: 10.1016/J.AUTCON.2015.12.018
  4. Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9-21. DOI: 10.1016/J.JNEUMETH.2003.10.009
  5. Derbali, L., & Frasson, C. (2011). Physiological evaluation of attention getting strategies during serious game play. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6738 LNAI, 447-449. DOI: 10.1007/978-3-642-21869-9_65/COVER
  6. Fernandez-Duque, D., Baird, J. A., & Posner, M. I. (2000). Executive Attention and Metacognitive Regulation. Consciousness and Cognition, 9(2), 288-307. DOI: 10.1006/CCOG.2000.0447
  7. Fernandez Rojas, R., Debie, E., Fidock, J., Barlow, M., Kasmarik, K., Anavatti, S., Garratt, M., & Abbass, H. (2020). Electroencephalographic Workload Indicators During Teleoperation of an Unmanned Aerial Vehicle Shepherding a Swarm of Unmanned Ground Vehicles in Contested Environments. Frontiers in Neuroscience, 14, 509340-509340. DOI: 10.3389/FNINS.2020.00040/BIBTEX
  8. Frank Moore, H., & Gheisari, M. (2019). A Review of Virtual and Mixed Reality Applications in Construction Safety Literature. Safety 2019, Vol. 5, Page 51, 5(3), 51-51. DOI: 10.3390/SAFETY5030051
  9. Ghasemy, H., Momtazpour, M., & Sardouie, S. H. (2019). Detection of Sustained Auditory Attention in Students with Visual Impairment. ICEE 2019 - 27th Iranian Conference on Electrical Engineering, 1798-1801. DOI: 10.1109/IRANIANCEE.2019.8786565
  10. Guo, H., Yu, Y., & Skitmore, M. (2017). Visualization technology-based construction safety management: A review. Automation in Construction, 73, 135-144. DOI: 10.1016/J.AUTCON.2016.10.004
  11. Hillard, B., El-Baz, A. S., Sears, L., Tasman, A., & Sokhadze, E. M. (2013). Neurofeedback training aimed to improve focused attention and alertness in children with ADHD: a study of relative power of EEG rhythms using custom-made software application. Clinical EEG and neuroscience, 44(3), 193-202. DOI: 10.1177/1550059412458262
  12. Homan, R. W., Herman, J., & Purdy, P. (1987). Cerebral location of international 10–20 system electrode placement. Electroencephalography and Clinical Neurophysiology, 66(4), 376-382. DOI: 10.1016/0013-4694(87)90206-9
  13. Huang, X., Hinze, J., & Asce, M. (2003). Analysis of Construction Worker Fall Accidents. Journal of Construction Engineering and Management, 129(3), 262-271. DOI: 10.1061/(ASCE)0733-9364(2003)129:3(262)
  14. Hwang, S., Jebelli, H., Choi, B., Choi, M., & Lee, S. (2018). Measuring Workers’ Emotional State during Construction Tasks Using Wearable EEG. Journal of Construction Engineering and Management, 144(7), 04018050-04018050. DOI: 10.1061/(ASCE)CO.1943-7862.0001506/ASSET/39CC5604-9C75-4146-8C0A-F7A212977F86/ASSETS/IMAGES/LARGE/FIGURE8.JPG
  15. James, W. (1890). The Principles of Psychology. Henry Holt. DOI: 10.1037/11059-000
  16. Jebelli, H., Hwang, S., & Lee, S. H. (2018). EEG-based workers' stress recognition at construction sites. Automation in Construction, 93, 315-324. DOI: 10.1016/J.AUTCON.2018.05.027
  17. Jeelani, I., Han, K., & Albert, A. (2020). Development of virtual reality and stereo-panoramic environments for construction safety training. Engineering, Construction and Architectural Management, 27(8), 1853-1876. DOI: 10.1108/ECAM-07-2019-0391/FULL/XML
  18. Kaushik, P., Moye, A., Vugt, M. v., & Roy, P. P. (2022). Decoding the cognitive states of attention and distraction in a real-life setting using EEG. Scientific Reports 2022 12:1, 12(1), 1-10. DOI: 10.1038/s41598-022-24417-w
  19. Ke, J., Zhang, M., Luo, X., & Chen, J. (2021). Monitoring distraction of construction workers caused by noise using a wearable Electroencephalography (EEG) device. Automation in Construction, 125, 103598-103598. DOI: 10.1016/J.AUTCON.2021.103598
  20. Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Research Reviews, 29(2-3), 169-195. DOI: 10.1016/S0165-0173(98)00056-3
  21. Mapelli, I., & Özkurt, T. E. (2019). Brain Oscillatory Correlates of Visual Short-Term Memory Errors. Frontiers in human neuroscience, 13. DOI: 10.3389/FNHUM.2019.00033
  22. Sacks, R., Perlman, A., & Barak, R. (2013). Construction safety training using immersive virtual reality. DOI: 10.1080/01446193.2013.828844
  23. Saedi, S., Fini, A. A. F., Khanzadi, M., Wong, J., Sheikhkhoshkar, M., & Banaei, M. (2022). Applications of electroencephalography in construction. Automation in Construction, 133, 103985-103985. DOI: 10.1016/J.AUTCON.2021.103985
  24. Sanei, S., & Chambers, J. (2007). EEG signal processing. 289-289. https://books.google.com/books/about/EEG_Signal_Processing.html?id=f44hLefOz6UC
  25. Sarailoo, R., Latifzadeh, K., Amiri, S. H., Bosaghzadeh, A., & Ebrahimpour, R. (2022). Assessment of instantaneous cognitive load imposed by educational multimedia using electroencephalography signals. Frontiers in Neuroscience, 16, 744737-744737. DOI: 10.3389/FNINS.2022.744737/BIBTEX
  26. Tehrani, B. M., Wang, J., & Truax, D. (2022). Assessment of mental fatigue using electroencephalography (EEG) and virtual reality (VR) for construction fall hazard prevention. Engineering, Construction and Architectural Management, 29(9), 3593-3616. DOI: 10.1108/ECAM-01-2021-0017/FULL/XML
  27. Top 10 Most Frequently Cited Standards | Occupational Safety and Health Administration. In.
  28. Ultracortex Mark IV | OpenBCI Documentation. In.
  29. Wilkins, J. R. (2011). Construction workers' perceptions of health and safety training programmes. Construction Management and Economics, 29(10), 1017-1026. DOI: 10.1080/01446193.2011.633538
  30. Winkler, I., Brandl, S., Horn, F., Waldburger, E., Allefeld, C., & Tangermann, M. (2014). Robust artifactual independent component classification for BCI practitioners. Journal of neural engineering, 11(3). DOI: 10.1088/1741-2560/11/3/035013
  31. Zietsch, B. P., Hansen, J. L., Hansell, N. K., Geffen, G. M., Martin, N. G., & Wright, M. J. (2007). Common and specific genetic influences on EEG power bands delta, theta, alpha, and beta. Biological Psychology, 75(2), 154-164. DOI: 10.1016/J.BIOPSYCHO.2007.01.004
  32. Zou, Z., & Ergan, S. (2023). Towards emotionally intelligent buildings: A Convolutional neural network based approach to classify human emotional experience in virtual built environments. Advanced Engineering Informatics, 55, 101868-101868. DOI: 10.1016/J.AEI.2022.101868
PDF
  • Publication Year: 2023
  • Pages: 231-241

XML
  • Publication Year: 2023

Chapter Information

Chapter Title

Cognitive Dynamics for Construction Management Learning Tasks in Mixed Reality Environments

Authors

Xuanchang Liu, Ivan Mutis

DOI

10.36253/979-12-215-0289-3.22

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

Book Subtitle

Managing the Digital Transformation of Construction Industry

Editors

Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/979-12-215-0289-3

eISBN (pdf)

979-12-215-0289-3

eISBN (xml)

979-12-215-0257-2

Series Title

Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

104

Fulltext
downloads

113

Views

Export Citation

1,339

Open Access Books

in the Catalogue

2,191

Book Chapters

3,763,352

Fulltext
downloads

4,396

Authors

from 923 Research Institutions

of 65 Nations

64

scientific boards

from 348 Research Institutions

of 43 Nations

1,246

Referees

from 379 Research Institutions

of 38 Nations