Investigating the Use of Two Low Cost Eye Tracking Systems for Detecting Pupillary Response to Changes in Mental Workload

2016 ◽  
Vol 60 (1) ◽  
pp. 37-41 ◽  
Author(s):  
Joseph Coyne ◽  
Ciara Sibley
Keyword(s):  
Eye Tracking ◽  
Pupil Size ◽  
Mental Workload ◽  
Low Cost ◽  
Digit Span ◽  
Pupillary Response ◽  
Background Luminance ◽  
Tracking Systems ◽  
Lighting Conditions ◽  
Size Data

Eye tracking technologies are being utilized at increasing rates within industry and research due to the very recent availability of low cost systems. This paper presents results from a study assessing two eye tracking systems, Gazepoint GP3 and Eye Tribe, both of which are available for under $500 and provide streaming gaze and pupil size data. The emphasis of this research was in evaluating the ability of these eye trackers to identify changes in pupil size which occur as a function of variations in lighting conditions as well as those associated with workload. Ten volunteers participated in an experiment in which a digit span task was employed to manipulate workload as user’s fixated on a monitor which varied in background luminance (black, gray and white). Results revealed that both systems were able to significantly differentiate pupil size differences in high and low workload trials and changes due to the monitor’s luminance. These findings are exceedingly promising for human factors researchers, as they open up the opportunity to augment studies with non-obtrusive, streaming measures of mental workload with technologies available for as little as $100.

scholarly journals Safe and sensible baseline correction of pupil-size data

2017 ◽  
Author(s):  
Sebastiaan Mathôt ◽  
Jasper Fabius ◽  
Elle van Heusden ◽  
Stefan Van der Stigchel
Keyword(s):  
Pupil Size ◽  
Statistical Power ◽  
Baseline Period ◽  
Experimental Manipulation ◽  
Pupillary Response ◽  
Baseline Correction ◽  
Data Loss ◽  
Eye Blinks ◽  
Random Fluctuations ◽  
Size Data

Measurement of pupil size (pupillometry) has recently gained renewed interest from psychologists, but there is little agreement on how pupil-size data is best analyzed. Here we focus on one aspect of pupillometric analyses: baseline correction, that is, analyzing changes in pupil size relative to a baseline period. Baseline correction is useful in experiments that investigate the effect of some experimental manipulation on pupil size. In such experiments, baseline correction improves statistical power by taking into account random fluctuations in pupil size over time. However, we show that baseline correction can also distort data if unrealistically small pupil sizes are recorded during the baseline period, which can easily occur due to eye blinks, data loss, or other distortions. Divisive baseline correction (corrected pupil size = pupil size / baseline) is affected more strongly by such distortions than subtractive baseline correction (corrected pupil size = pupil size - baseline). We make four recommendations for safe and sensible baseline correction of pupil-size data: 1) use subtractive baseline correction; 2) visually compare your corrected and uncorrected data; 3) be wary of pupil-size effects that emerge faster than the latency of the pupillary response allows (within ±220 ms after the manipulation that induces the effect); and 4) remove trials on which baseline pupil size is unrealistically small (indicative of blinks and other distortions).

Low Cost Eye Tracking: Ready for Individual Differences Research?

2018 ◽  
Vol 62 (1) ◽  
pp. 741-745 ◽  
Author(s):  
Ciara Sibley ◽  
Cyrus Foroughi ◽  
Noelle Brown ◽  
Joseph T. Coyne
Keyword(s):  
Working Memory ◽  
Eye Tracking ◽  
Pupil Size ◽  
Low Cost ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Cognitive Components ◽  
Student Pilots ◽  
Partial Operation ◽  
Selection Test

The recent availability of low cost of eye tracking hardware provides researchers a fruitful opportunity to collect additional human subject data for under $700. This current study sought to investigate whether low-cost eye tracking is capable of replicating a large effect showing a relationship between resting pupil size and working memory capacity. Seventy-nine Navy and Marine Corps student pilots participated in this study and granted access to their aviation selection test scores. The study demonstrated the capability of the Gazepoint GP3 system to detect the pupillary light reflex within every participant. However, in contrast to findings from other researchers, analyses revealed a negative correlation between resting pupil size and partial Operation Span scores and no correlation between resting pupil size and two cognitive components of the aviation selection test. These findings, in addition to other reasons discussed herein, suggest that the Gazepoint GP3 system’s millimeter pupil size measurements should not be used in isolation to compare values between individuals. They also suggest the need for further investigation of the relationship between baseline pupil size and working memory capacity.

scholarly journals Toward graphene textiles in wearable eye tracking systems for human–machine interaction

2021 ◽  
Vol 12 ◽  
pp. 180-189
Author(s):  
Ata Jedari Golparvar ◽  
Murat Kaya Yapici
Keyword(s):  
Eye Tracking ◽  
Single Channel ◽  
Low Cost ◽  
Assistive Device ◽  
Time Signal ◽  
Tracking Systems ◽  
Stream Data ◽  
Human Machine Interaction ◽  
Mouse Cursor ◽  
Machine Interaction

The study of eye movements and the measurement of the resulting biopotential, referred to as electrooculography (EOG), may find increasing use in applications within the domain of activity recognition, context awareness, mobile human–computer and human–machine interaction (HCI/HMI), and personal medical devices; provided that, seamless sensing of eye activity and processing thereof is achieved by a truly wearable, low-cost, and accessible technology. The present study demonstrates an alternative to the bulky and expensive camera-based eye tracking systems and reports the development of a graphene textile-based personal assistive device for the first time. This self-contained wearable prototype comprises a headband with soft graphene textile electrodes that overcome the limitations of conventional “wet” electrodes, along with miniaturized, portable readout electronics with real-time signal processing capability that can stream data to a remote device over Bluetooth. The potential of graphene textiles in wearable eye tracking and eye-operated remote object interaction is demonstrated by controlling a mouse cursor on screen for typing with a virtual keyboard and enabling navigation of a four-wheeled robot in a maze, all utilizing five different eye motions initiated with a single channel EOG acquisition. Typing speeds of up to six characters per minute without prediction algorithms and guidance of the robot in a maze with four 180° turns were successfully achieved with perfect pattern detection accuracies of 100% and 98%, respectively.

scholarly journals Safe and sensible baseline correction of pupil-size data

2017 ◽  
Author(s):  
Sebastiaan Mathôt ◽  
Jasper Fabius ◽  
Elle van Heusden ◽  
Stefan Van der Stigchel
Keyword(s):  
Pupil Size ◽  
Statistical Power ◽  
Baseline Period ◽  
Experimental Manipulation ◽  
Pupillary Response ◽  
Baseline Correction ◽  
Data Loss ◽  
Eye Blinks ◽  
Random Fluctuations ◽  
Size Data

Measurement of pupil size (pupillometry) has recently gained renewed interest from psychologists, but there is little agreement on how pupil-size data is best analyzed. Here we focus on one aspect of pupillometric analyses: baseline correction, that is, analyzing changes in pupil size relative to a baseline period. Baseline correction is useful in experiments that investigate the effect of some experimental manipulation on pupil size. In such experiments, baseline correction improves statistical power by taking into account random fluctuations in pupil size over time. However, we show that baseline correction can also distort data if unrealistically small pupil sizes are recorded during the baseline period, which can easily occur due to eye blinks, data loss, or other distortions. Divisive baseline correction (corrected pupil size = pupil size / baseline) is affected more strongly by such distortions than subtractive baseline correction (corrected pupil size = pupil size - baseline). We make four recommendations for safe and sensible baseline correction of pupil-size data: 1) use subtractive baseline correction; 2) visually compare your corrected and uncorrected data; 3) be wary of pupil-size effects that emerge faster than the latency of the pupillary response allows (within ±220 ms after the manipulation that induces the effect); and 4) remove trials on which baseline pupil size is unrealistically small (indicative of blinks and other distortions).

scholarly journals A Fast and Effective System for Analysis of Optokinetic Waveforms with a Low-Cost Eye Tracking Device

Healthcare ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 10
Author(s):  
Chong-Bin Tsai ◽  
Wei-Yu Hung ◽  
Wei-Yen Hsu
Keyword(s):  
Eye Movements ◽  
Eye Tracking ◽  
Eye Movement ◽  
Low Cost ◽  
Saccadic Eye Movements ◽  
Slow Phase ◽  
Eye Tracker ◽  
Effective System ◽  
Tracking Device ◽  
Traditional Approaches

Optokinetic nystagmus (OKN) is an involuntary eye movement induced by motion of a large proportion of the visual field. It consists of a “slow phase (SP)” with eye movements in the same direction as the movement of the pattern and a “fast phase (FP)” with saccadic eye movements in the opposite direction. Study of OKN can reveal valuable information in ophthalmology, neurology and psychology. However, the current commercially available high-resolution and research-grade eye tracker is usually expensive. Methods & Results: We developed a novel fast and effective system combined with a low-cost eye tracking device to accurately quantitatively measure OKN eye movement. Conclusions: The experimental results indicate that the proposed method achieves fast and promising results in comparisons with several traditional approaches.

scholarly journals Accurate Model-Based Point of Gaze Estimation on Mobile Devices

Vision ◽  
2018 ◽  
Vol 2 (3) ◽  
pp. 35 ◽  
Author(s):  
Braiden Brousseau ◽  
Jonathan Rose ◽  
Moshe Eizenman
Keyword(s):  
Eye Tracking ◽  
Mobile Devices ◽  
Head Movements ◽  
Roll Angle ◽  
New Method ◽  
Estimation Methods ◽  
Infrared Light ◽  
Gaze Estimation ◽  
Tracking Systems ◽  
Estimation Models

The most accurate remote Point of Gaze (PoG) estimation methods that allow free head movements use infrared light sources and cameras together with gaze estimation models. Current gaze estimation models were developed for desktop eye-tracking systems and assume that the relative roll between the system and the subjects’ eyes (the ’R-Roll’) is roughly constant during use. This assumption is not true for hand-held mobile-device-based eye-tracking systems. We present an analysis that shows the accuracy of estimating the PoG on screens of hand-held mobile devices depends on the magnitude of the R-Roll angle and the angular offset between the visual and optical axes of the individual viewer. We also describe a new method to determine the PoG which compensates for the effects of R-Roll on the accuracy of the POG. Experimental results on a prototype infrared smartphone show that for an R-Roll angle of 90 ° , the new method achieves accuracy of approximately 1 ° , while a gaze estimation method that assumes that the R-Roll angle remains constant achieves an accuracy of 3.5 ° . The manner in which the experimental PoG estimation errors increase with the increase in the R-Roll angle was consistent with the analysis. The method presented in this paper can improve significantly the performance of eye-tracking systems on hand-held mobile-devices.

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