journal article·2021-10-28Open access

Calculating and Analyzing Angular Head Jerk in Augmented and Virtual Reality: Effect of AR Cue Design on Angular Jerk

Jared Van Dam, Kyle Tanous, Matt Werner, Joseph L. Gabbard

Applied Sciences· Volume 11· Issue 21

Abstract

In this work, we propose a convenient method for evaluating levels of angular jerk in augmented reality (AR) and virtual reality (VR). Jerk is a rarely analyzed metric in usability studies, although it can be measured and calculated easily with most head-worn displays and can yield highly relevant information to designers. Here, we developed and implemented a system capable of calculating and analyzing jerk in real-time based on orientation data from an off-the-shelf head-worn display. An experiment was then carried out to determine whether the presence of AR user interface annotations results in changes to users’ angular head jerk when conducting a time-pressured visual search task. Analysis of the data indicates that a decrease in jerk is significantly associated with the use of AR augmentations. As noted in the limitations section, however, the conclusions drawn from this work should be limited, as this analysis method is novel in the VR/AR space and because of methodological limitations that limited the reliability of the jerk data. The work presented herein considerably facilitates the use of jerk as a quick component measure of usability and serves as an initial point off which future research involving jerk in VR and AR can be performed.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Virtual Reality Applications and Impacts · Spatial Cognition and Navigation · Ergonomics and Musculoskeletal Disorders

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA 24060, USA

Department of Aerospace and Ocean Engineering, Virginia Tech, Blacksburg, VA 24060, USA

Funding

National Science Foundation

References (20)

Show source-provided references
1. Lu, F., and Bowman, D.A. (2021, January 27). Evaluating the potential of glanceable AR interfaces for authentic everyday uses. Proceedings of the 2021 IEEE Virtual Reality and 3D User Interfaces (VR), VR 2021, Lisbon, Portugal.10.1109/VR50410.2021.00104 ↗
2. Jerk as an Indicator of Physical Exertion and Fatigue10.1016/j.autcon.2019.04.016 ↗
3. Beyond Velocity and Acceleration: Jerk, Snap and Higher Derivatives10.1088/0143-0807/37/6/065008 ↗
4. Estimating Movement Smoothness From Inertial Measurement Units
5. Vestibular System: The Many Facets of a Multimodal Sense10.1146/annurev.neuro.31.060407.125555 ↗
6. Semicircular canal biomechanics in health and disease10.1152/jn.00708.2018 ↗
7. Van Dam, J., Krasner, A., and Gabbard, J.L. (2020, January 22–26). Drone-based Augmented Reality Platform for Bridge Inspection: Effect of AR Cue Design on Visual Search Tasks. Proceedings of the 2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), Atlanta, GA, USA.10.1109/VRW50115.2020.00043 ↗
8. Othman, N., and Romli, F.I. (2016). Mental Workload Evaluation of Pilots Using Pupil Dilation. Int. Rev. Aerosp. Eng., 9.10.15866/irease.v9i3.9541 ↗
9. Potential Eye Tracking Metrics and Indicators to Measure Cognitive Load in Human-Computer Interaction Research
10. Kholinne, E., Gandhi, M.J., Adikrishna, A., Hong, H., Kim, H., Hong, J., and Jeon, I.-H. (2018). The Dimensionless Squared Jerk: An Objective Parameter That Improves Assessment of Hand Motion Analysis during Simulated Shoulder Arthroscopy. Biomed. Res. Int.10.1155/2018/7816160 ↗
11. Zhang, L., Diraneyya, M.M., Ryu, J., Haas, C.T., and Abdel-Rahman, E. (2018, January 26–29). Assessment of Jerk As a Method of Physical Fatigue Detection. Proceedings of the ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Quebec City, QC, Canada.10.1115/DETC2018-86289 ↗
12. Utilizing Steering Entropy as a Measure of Performance in Head-mounted Augmented Reality
13. The Effect of Head Tracking on the Degree of Presence in Virtual Reality10.1080/10447318.2018.1555736 ↗
14. A Data-driven Approach to Modeling Physical Fatigue in the Workplace Using Wearable Sensors10.1016/j.apergo.2017.02.001 ↗
15. Zhang, L., Diraneyya, M.M., Ryu, J., Haas, C.T., and Abdel-Rahman, E. (2019, January 21–24). Automated monitoring of physical fatigue using jerk. Proceedings of the 36th International Symposium on Automation and Robotics in Construction (ISARC), Banff, AB, Canada.10.22260/ISARC2019/0132 ↗
16. Can vehicle longitudinal jerk be used to identify aggressive drivers? An examination using naturalistic driving data10.1016/j.aap.2017.04.012 ↗
17. Hämäläinen, W., Järvinen, M., Martiskainen, P., and Mononen, J. (2011, January 22–24). Jerk-based Feature Extraction for Robust Activity Recognition from Acceleration Data. Proceedings of the 11th International Conference on Intelligent Systems Design and Applications, Cordoba, Spain.10.1109/ISDA.2011.6121760 ↗
18. Metaheuristic Optimized Edge Detection for Recognition of Concrete Wall Cracks: A Comparative Study on the Performances of Roberts, Prewitt, Canny, and Sobel Algorithms
19. Image Processing-Based Recognition of Wall Defects Using Machine Learning Approaches and Steerable Filters10.1155/2018/7913952 ↗
20. Lu, W., Duh, B.L.H., and Feiner, S. (2012, January 5–8). Subtle Cueing for Visual Search in Augmented Reality. Proceedings of the 2012 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), Atlanta, GA, USA.10.1109/ISMAR.2012.6402553 ↗