3D eye movement analysis for VR visual inspection training

Duchowski, Andrew T.; Medlin, Eric; Cournia, Nathan; Gramopadhye, Anand K.; Melloy, Brian J.; Nair, Santosh

doi:10.1145/507072.507094

Cited by 33 publications

(17 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Performance measures report accuracy (the number of defects detected) and efficiency (speed; time taken for the task in seconds). Process measures were obtained by analyzing the raw point of regard data of the participants, recorded by the eye tracking equipment [7].…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Use of eye movements as feedforward training for a synthetic aircraft inspection task

Sadasivan

Greenstein

Gramopadhye

et al. 2005

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Self Cite

View full text Add to dashboard Cite

Aircraft inspection is a vital element in assuring safety and reliability of the air transportation system. The human inspector performing visual inspection of an aircraft is the backbone of this process and training is an effective strategy for improving their inspection performance. Previous studies have shown offline feedback training to be effective in improving subsequent visual inspection performance. Because experienced inspectors are known to adopt a better inspection strategy than novices, providing visualization of experts' cognitive processes a priori can accelerate novices' adoption of the experts' strategy. Using eye tracking equipment, we record the point of regard of an expert inspector performing an inspection task in a virtual reality simulator. Analysis of their eye movements leads to a visualization of their scanpaths and allows us to display the inspector's visual search (hence cognitive) strategy. We show how providing this type of scanpath-based feedforward training of novices leads to improved accuracy performance in the simulator coupled with an observed speed-accuracy tradeoff. We contend that the tradeoff results from trained novices adopting a slower paced strategy through increased fixation durations, suggesting trained novices learn a more deliberate target search/discrimination strategy that requires more time to execute. ACM Classification:H5.1; H1.2. Information interfaces and presentation (e.g., HCI): Multimedia information systems; Models and principles: User/machine systems (human factors).

show abstract

Section: Resultsmentioning

confidence: 99%

“…A virtual reality aircraft inspection simulator developed previously was used to carry out the experiment [8,7]. The software component of this simulator consists of two programs, Inspector and Vspec.…”

Section: Stimulus Materials and Equipmentmentioning

confidence: 99%

Use of eye movements as feedforward training for a synthetic aircraft inspection task

Sadasivan

Greenstein

Gramopadhye

et al. 2005

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…Tanriverdi and Jacob [2000] used gaze as an interaction method in an immersive virtual reality environment presented via head-mounted display to select objects arranged freely in 3D space. Others used gaze for collaboration in virtual reality [Duchowski et al 2004], for inspection task training [Duchowski et al 2002] or for system interactions [Gepner et al 2007]. All these approaches have in common that they have the capability to classify the objects of interest in real-time, but they require an artificial presentation of the target objects to achieve this.…”

Section: Geometry-based Approachesmentioning

confidence: 99%

EyeSee3D

Pfeiffer

Renner

2014

Proceedings of the Symposium on Eye Tracking Research and Applications

View full text Add to dashboard Cite

For validly analyzing human visual attention, it is often necessary to proceed from computer-based desktop set-ups to more natural real-world settings. However, the resulting loss of control has to be counterbalanced by increasing participant and/or item count. Together with the effort required to manually annotate the gaze-cursor videos recorded with mobile eye trackers, this renders many studies unfeasible.We tackle this issue by minimizing the need for manual annotation of mobile gaze data. Our approach combines geometric modelling with inexpensive 3D marker tracking to align virtual proxies with the real-world objects. This allows us to classify fixations on objects of interest automatically while supporting a completely free moving participant.The paper presents the EyeSee3D method as well as a comparison of an expensive outside-in (external cameras) and a low-cost inside-out (scene camera) tracking of the eyetracker's position. The EyeSee3D approach is evaluated comparing the results from automatic and manual classification of fixation targets, which raises old problems of annotation validity in a modern context.

show abstract

“…Daugherty et al [2010] were one of the first to use a commodity desktop binocular eye tracker to measure gaze vergence. Earlier efforts also measured vergence, but they used more specialized equipment [Essig et al 2004;Medlin 2003;Duchowski et al 2002].…”

Section: Introductionmentioning

confidence: 99%

Comparing estimated gaze depth in virtual and physical environments

Duchowski

House

Gestring

et al. 2014

Proceedings of the Symposium on Eye Tracking Research and Applications

Self Cite

View full text Add to dashboard Cite

a) Wheatstone-style haploscope used for the virtual environment. (b) Dikablis/Vicon system used for the physical environment.Figure 1: Virtual and physical environments used in the study to compare vergence estimates. AbstractWe show that the error in 3D gaze depth (vergence) estimated from binocularly-tracked gaze disparity is related to the viewing distance of the screen calibration plane at which 2D gaze is recorded. In a stereoscopic (virtual) environment, this relationship is evident in gaze to target depth error: vergence error behind the screen is greater than in front of the screen and is lowest at the screen depth. In a physical environment, with no accommodation-vergence conflict, the magnitude of vergence error in front of the 2D calibration plane appears reversed, increasing with distance from the viewer.

show abstract

3D eye movement analysis for VR visual inspection training

Cited by 33 publications

References 14 publications

Use of eye movements as feedforward training for a synthetic aircraft inspection task

Use of eye movements as feedforward training for a synthetic aircraft inspection task

EyeSee3D

Comparing estimated gaze depth in virtual and physical environments

Contact Info

Product

Resources

About