Export file:


  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text


  • Citation Only
  • Citation and Abstract

Augmented Reality Device Operator Cognitive Strain Determination and Prediction

Department of Computer Science, Central Michigan University, Pearce Hall 413, Mount Pleasant, MI 48859, USA

Special Issues: Augmented and Virtual Reality for Industry 4.0

Augmented reality (AR) solutions are in the process of entering a broad variety of industries to modify the capabilities of workers through (close to) real-time display of context-dependent information. An example for real-time training is the display of instructional materials, such as manuals, for operation and maintenance. Especially in industrial settings, this will allow for the enhancement of workforce capabilities in real-time. However, little is known with respect to the cognitive load that is incurred as a result of this process, which might hinder the realization of desired outcomes. In this paper, we evaluate visual tasks with respect to the cognitive load based on electroencephalography (EEG) employing existing and new metrics utilizing a publicly available data set. In turn, we provide an initial quantified and directly measured approach. We find that overall results are highly subjective, but already available commercial equipment can readily be employed to determine the cognitive load with R2 scores around 0.5 when utilizing k-nearest-neighbor (KNN) approaches directly. More intricate metrics at different measurement points could thus help detect and alleviate undesired stressors in industrial augmented reality settings.
  Article Metrics


1. Pierdicca R, Frontoni E, Pollini R, et al. (2017) The Use of Augmented Reality Glasses for the Application in Industry 4.0. In: De Paolis L, Bourdot P, Mongelli A. Editors. Augmented Reality, Virtual Reality, and Computer Graphics, Cham, Switzerland: Springer, 389-401.

2. Gabbard J, Fitch G, Kim H (2014) Behind the Glass: Driver Challenges and Opportunities for AR Automotive Applications. P IEEE 102: 124-136.    

3. Rolland J, Fuchs H (2000) Optical Versus Video See-Through Head-Mounted Displays in Medical Visualization Presence. Presence-Teleop Virt 9: 287-309.    

4. Traub J, Sielhorst T, Heining S, et al. (2008) Advanced Display and Visualization Concepts for Image Guided Surgery. J Disp Technol 4: 483-490.    

5. Clini P, Frontoni E, Quattrini R, et al. (2014) Augmented Reality Experience: From High-resolution Acquisition to Real Time Augmented Contents. Adv Multimedia 2014: 9.

6. Lee K (2012) Augmented Reality in Education and Training. Techtrends 56: 13-21.

7. Backs R, Boucsein W (2000) Author, Engineering Psychophysiology: Issues and Applications. Mahwah, NJ, USA: Lawrence Erlbaum.

8. Gevins A, Smith M, McEvoy L, et al. (1997) High-resolution EEG mapping of cortical activation related to working memory: effects of task difficulty, type of processing, and practice. Cereb Cortex 7: 374-385.    

9. Sweller J (1988) Cognitive Load During Problem Solving: Effects on Learning. Cognitive science 12: 257-285.    

10. Kumar N, Kumar J (2016) Measurement of Cognitive Load in HCI Systems Using EEG Power Spectrum: An Experimental Study. Procedia Computer Science 84: 70-78.    

11. Gevins A, Smith M, Leong H, et al. (1998) Monitoring Working Memory Load during Computer-Based Tasks with EEG Pattern Recognition Methods. Human Factors 40: 79-91.    

12. Mazher M, Aziz A, Malik A, et al. (2017) An EEG-Based Cognitive Load Assessment in Multimedia Learning Using Feature Extraction and Partial Directed Coherence. IEEE Access 5: 14819-14829.    

13. Holm A, Lukander K, Korpela J, et al. (2009) Estimating Brain Load from the EEG. The Scientific World J 9: 639-651.    

14. Plechawska-W´ojcik M, Wawrzyk M, Wesołowska K, et al. (2017) EEG spectral analysis of human cognitive workload study. Studia Informatica 38: 17-30.

15. Bauman B, Seeling P (2017) Visual Interface Evaluation for Wearables Datasets: Predicting the Subjective Augmented Vision Image QoE and QoS. Future Internet 9: 40.    

16. Oxkey B (2017) International 10-20 system for EEG electrode placement, showing modified combinatorial nomenclature. Available from: https://commons.wikimedia.org/wiki/File:International_10-20_system_for_ EEG-MCN.svg.

17. Bonanni L Lee C-H, Selker T (2005) Attention-based Design of Augmented Reality Interfaces. Extended Abstracts on Human Factors in Computing Systems Portland, OR, USA: 1228-1231.

18. Hart SG (2006) NASA-task load index (NASA-TLX); 20 years later. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 50: 904-908.    

19. Rentzos L, Vourtsis C, Mavrikios D, Chryssolouris G (2014) Using VR for Complex Product Design. In: Shumaker R and Lackey S (eds) Virtual, Augmented and Mixed Reality. Proceedings of the 6th International Conference Applications of Virtual and Augmented Reality Crete, Greece: 455-464.

20. Nee AYC, Ong SK, Chryssolouris G, Mourtzis D (2012) Augmented reality applications in design and manufacturing. CIRP Annals 61: 657-679.    

21. Makris S, Karagiannis P, Koukas S, Matthaiakis A-S (2016) Augmented reality system for operator support in humanrobot collaborative assembly. CIRP Annals 65: 61-64.    

22. Makris S, Pintzos G, Rentzos L, Chryssolouris G (2013) Assembly support using AR technology based on automatic sequence generation. CIRP Annals 62: 9-12.    

Copyright Info: © 2017, Patrick Seeling, licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Article outline

Show full outline
Copyright © AIMS Press All Rights Reserved