Research

9 pages, 1209 KiB

Open AccessArticle

Improving Performance of the Human Pupil Orbit Model (HPOM) Estimation Method for Eye-Gaze Tracking

by Seungbong Lee, Jaehoon Jeong, Nahyun Kim, Manjae Shin and Sungmin Kim

Sensors 2022, 22(23), 9398; https://doi.org/10.3390/s22239398 - 02 Dec 2022

Viewed by 1414

Eye-gaze direction-tracking technology is used in fields such as medicine, education, engineering, and gaming. Stability, accuracy, and precision of eye-gaze direction-tracking are demanded with simultaneous upgrades in response speed. In this study, a method is proposed to improve the speed with decreases in [...] Read more.

Eye-gaze direction-tracking technology is used in fields such as medicine, education, engineering, and gaming. Stability, accuracy, and precision of eye-gaze direction-tracking are demanded with simultaneous upgrades in response speed. In this study, a method is proposed to improve the speed with decreases in the system load and precision in the human pupil orbit model (HPOM) estimation method. The new method was proposed based on the phenomenon that the minor axis of the elliptical-deformed pupil always pointed toward the rotational center presented in various eye-gaze direction detection studies and HPOM estimation methods. Simulation experimental results confirmed that the speed was improved by at least 74 times by consuming less than 7 ms compared to the HPOM estimation. The accuracy of the eye’s ocular rotational center point showed a maximum error of approximately 0.2 pixels on the x-axis and approximately 8 pixels on the y-axis. The precision of the proposed method was 0.0 pixels when the number of estimation samples (ES) was 7 or less, which showed results consistent with those of the HPOM estimation studies. However, the proposed method was judged to work conservatively against the allowable angle error (AAE), considering that the experiment was conducted under the worst conditions and the cost used to estimate the final model. Therefore, the proposed method could estimate HPOM with high accuracy and precision through AAE adjustment according to system performance and the usage environment. Full article

(This article belongs to the Special Issue Wearable Technologies and Applications for Eye Tracking)

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18 pages, 5118 KiB

Open AccessArticle

An Investigation on the Influence of Operation Experience on Virtual Hazard Perception Using Wearable Eye Tracking Technology

by Siyu Li, Yongqing Jiang, Chao Sun, Kangkang Guo and Xin Wang

Sensors 2022, 22(14), 5115; https://doi.org/10.3390/s22145115 - 07 Jul 2022

Cited by 2 | Viewed by 1495

Abstract

Poor electrical hazard recognition is a widespread issue in the production industry. Hazard perception has impacts workers’ hazard recognition, causing them to experience unanticipated hazard exposure and suffer catastrophic injuries. To improve the factors of affecting hazard perception, the current study examined hazard [...] Read more.

Poor electrical hazard recognition is a widespread issue in the production industry. Hazard perception has impacts workers’ hazard recognition, causing them to experience unanticipated hazard exposure and suffer catastrophic injuries. To improve the factors of affecting hazard perception, the current study examined hazard recognition as an everyday visual search task. A comparative test was carried out combining the advantages and disadvantages of the two test methods. It was confirmed that the virtual image test data can replace the real image test data and demonstrate superior flexible settings performance, so the virtual image test method is used. A hazard perception test method based on wearable eye tracking technology was proposed to analyze the eye-tracking data (i.e., fixation, count, search duration, mean fixation duration, eye tracking, and hazard recognition performance feedback) were compared between experts in the field of electrical safety: skilled workers with at least five years of work experience and workers who had been on the job for less than a year. It was found that experts had a better hazard recognition accuracy and missed detection rate than other workers. Experts’ hazards research track was more concised and paid less attention time. This advantage is most obvious in complex risk environments. The findings also suggest that workers who have different working years was not obvious visual search patterns other than the search duration. As can be seen the work experience is not an absolute factor in improving hazard perception. The present research will be useful to understand the influence of working years on hazard perception and provide a theoretical basis for corresponding training. Full article

(This article belongs to the Special Issue Wearable Technologies and Applications for Eye Tracking)

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22 pages, 4564 KiB

Open AccessArticle

Exploring Eye Movement Biometrics in Real-World Activities: A Case Study of Wayfinding

by Hua Liao, Wendi Zhao, Changbo Zhang and Weihua Dong

Sensors 2022, 22(8), 2949; https://doi.org/10.3390/s22082949 - 12 Apr 2022

Cited by 3 | Viewed by 2509

Abstract

Eye movement biometrics can enable continuous verification for highly secure environments such as financial transactions and defense establishments, as well as a more personalized and tailored experience in gaze-based human–computer interactions. However, there are numerous challenges to recognizing people in real environments using [...] Read more.

Eye movement biometrics can enable continuous verification for highly secure environments such as financial transactions and defense establishments, as well as a more personalized and tailored experience in gaze-based human–computer interactions. However, there are numerous challenges to recognizing people in real environments using eye movements, such as implicity and stimulus independence. In the instance of wayfinding, this research intends to investigate implicit and stimulus-independent eye movement biometrics in real-world situations. We collected 39 subjects’ eye movement data from real-world wayfinding experiments and derived five sets of eye movement features (the basic statistical, pupillary response, fixation density, fixation semantic and saccade encoding features). We adopted a random forest and performed biometric recognition for both identification and verification scenarios. The best accuracy we obtained in the identification scenario was 78% (equal error rate, EER = 6.3%) with the 10-fold classification and 64% (EER = 12.1%) with the leave-one-route-out classification. The best accuracy we achieved in the verification scenario was 89% (EER = 9.1%). Additionally, we tested performance across the 5 feature sets and 20 time window sizes. The results showed that the verification accuracy was insensitive to the increase in the time window size. These findings are the first indication of the viability of performing implicit and stimulus-independent biometric recognition in real-world settings using wearable eye tracking. Full article

(This article belongs to the Special Issue Wearable Technologies and Applications for Eye Tracking)

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16 pages, 806 KiB

Open AccessArticle

Eye Movement Alterations in Post-COVID-19 Condition: A Proof-of-Concept Study

by Cecilia García Cena, Mariana Campos Costa, Roque Saltarén Pazmiño, Cristina Peixoto Santos, David Gómez-Andrés and Julián Benito-León

Sensors 2022, 22(4), 1481; https://doi.org/10.3390/s22041481 - 14 Feb 2022

Cited by 9 | Viewed by 2590

Abstract

There is much evidence pointing out eye movement alterations in several neurological diseases. To the best of our knowledge, this is the first video-oculography study describing potential alterations of eye movements in the post-COVID-19 condition. Visually guided saccades, memory-guided saccades, and antisaccades in [...] Read more.

There is much evidence pointing out eye movement alterations in several neurological diseases. To the best of our knowledge, this is the first video-oculography study describing potential alterations of eye movements in the post-COVID-19 condition. Visually guided saccades, memory-guided saccades, and antisaccades in horizontal axis were measured. In all visual tests, the stimulus was deployed with a gap condition. The duration of the test was between 5 and 7 min per participant. A group of

n = 9

patients with the post-COVID-19 condition was included in this study. Values were compared with a group (

n = 9

) of healthy volunteers whom the SARS-CoV-2 virus had not infected. Features such as centripetal and centrifugal latencies, success rates in memory saccades, antisaccades, and blinks were computed. We found that patients with the post-COVID-19 condition had eye movement alterations mainly in centripetal latency in visually guided saccades, the success rate in memory-guided saccade test, latency in antisaccades, and its standard deviation, which suggests the involvement of frontoparietal networks. Further work is required to understand these eye movements’ alterations and their functional consequences. Full article

(This article belongs to the Special Issue Wearable Technologies and Applications for Eye Tracking)

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15 pages, 3259 KiB

Open AccessArticle

Assessing Perceptual Load and Cognitive Load by Fixation-Related Information of Eye Movements

by Jung-Chun Liu, Kuei-An Li, Su-Ling Yeh and Shao-Yi Chien

Sensors 2022, 22(3), 1187; https://doi.org/10.3390/s22031187 - 04 Feb 2022

Cited by 18 | Viewed by 2963

Abstract

Assessing mental workload is imperative for avoiding unintended negative consequences in critical situations such as driving and piloting. To evaluate mental workload, measures of eye movements have been adopted, but unequivocal results remain elusive, especially those related to fixation-related parameters. We aimed to [...] Read more.

Assessing mental workload is imperative for avoiding unintended negative consequences in critical situations such as driving and piloting. To evaluate mental workload, measures of eye movements have been adopted, but unequivocal results remain elusive, especially those related to fixation-related parameters. We aimed to resolve the discrepancy of previous results by differentiating two kinds of mental workload (perceptual load and cognitive load) and manipulated them independently using a modified video game. We found opposite effects of the two kinds of mental workload on fixation-related parameters: shorter fixation durations and more fixations when participants played an episode with high (vs. low) perceptual load, and longer fixation durations and fewer fixations when they played an episode with high (vs. low) cognitive load. Such opposite effects were in line with the load theory and demonstrated that fixation-related parameters can be used to index mental workload at different (perceptual and cognitive) stages of mental processing. Full article

(This article belongs to the Special Issue Wearable Technologies and Applications for Eye Tracking)

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21 pages, 1481 KiB

Open AccessArticle

Automatic Visual Attention Detection for Mobile Eye Tracking Using Pre-Trained Computer Vision Models and Human Gaze

by Michael Barz and Daniel Sonntag

Sensors 2021, 21(12), 4143; https://doi.org/10.3390/s21124143 - 16 Jun 2021

Cited by 12 | Viewed by 5521

Abstract

Processing visual stimuli in a scene is essential for the human brain to make situation-aware decisions. These stimuli, which are prevalent subjects of diagnostic eye tracking studies, are commonly encoded as rectangular areas of interest (AOIs) per frame. Because it is a tedious [...] Read more.

Processing visual stimuli in a scene is essential for the human brain to make situation-aware decisions. These stimuli, which are prevalent subjects of diagnostic eye tracking studies, are commonly encoded as rectangular areas of interest (AOIs) per frame. Because it is a tedious manual annotation task, the automatic detection and annotation of visual attention to AOIs can accelerate and objectify eye tracking research, in particular for mobile eye tracking with egocentric video feeds. In this work, we implement two methods to automatically detect visual attention to AOIs using pre-trained deep learning models for image classification and object detection. Furthermore, we develop an evaluation framework based on the VISUS dataset and well-known performance metrics from the field of activity recognition. We systematically evaluate our methods within this framework, discuss potentials and limitations, and propose ways to improve the performance of future automatic visual attention detection methods. Full article

(This article belongs to the Special Issue Wearable Technologies and Applications for Eye Tracking)

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18 pages, 2185 KiB

Open AccessEditor’s ChoiceArticle

ARETT: Augmented Reality Eye Tracking Toolkit for Head Mounted Displays

by Sebastian Kapp, Michael Barz, Sergey Mukhametov, Daniel Sonntag and Jochen Kuhn

Sensors 2021, 21(6), 2234; https://doi.org/10.3390/s21062234 - 23 Mar 2021

Cited by 51 | Viewed by 8335

Abstract

Currently an increasing number of head mounted displays (HMD) for virtual and augmented reality (VR/AR) are equipped with integrated eye trackers. Use cases of these integrated eye trackers include rendering optimization and gaze-based user interaction. In addition, visual attention in VR and AR [...] Read more.

Currently an increasing number of head mounted displays (HMD) for virtual and augmented reality (VR/AR) are equipped with integrated eye trackers. Use cases of these integrated eye trackers include rendering optimization and gaze-based user interaction. In addition, visual attention in VR and AR is interesting for applied research based on eye tracking in cognitive or educational sciences for example. While some research toolkits for VR already exist, only a few target AR scenarios. In this work, we present an open-source eye tracking toolkit for reliable gaze data acquisition in AR based on Unity 3D and the Microsoft HoloLens 2, as well as an R package for seamless data analysis. Furthermore, we evaluate the spatial accuracy and precision of the integrated eye tracker for fixation targets with different distances and angles to the user (

n = 21

). On average, we found that gaze estimates are reported with an angular accuracy of

0.83

degrees and a precision of

0.27

degrees while the user is resting, which is on par with state-of-the-art mobile eye trackers. Full article

(This article belongs to the Special Issue Wearable Technologies and Applications for Eye Tracking)

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15 pages, 1885 KiB

Open AccessArticle

Development and Calibration of an Eye-Tracking Fixation Identification Algorithm for Immersive Virtual Reality

by Jose Llanes-Jurado, Javier Marín-Morales, Jaime Guixeres and Mariano Alcañiz

Sensors 2020, 20(17), 4956; https://doi.org/10.3390/s20174956 - 01 Sep 2020

Cited by 31 | Viewed by 5626

Abstract

Fixation identification is an essential task in the extraction of relevant information from gaze patterns; various algorithms are used in the identification process. However, the thresholds used in the algorithms greatly affect their sensitivity. Moreover, the application of these algorithm to eye-tracking technologies [...] Read more.

Fixation identification is an essential task in the extraction of relevant information from gaze patterns; various algorithms are used in the identification process. However, the thresholds used in the algorithms greatly affect their sensitivity. Moreover, the application of these algorithm to eye-tracking technologies integrated into head-mounted displays, where the subject’s head position is unrestricted, is still an open issue. Therefore, the adaptation of eye-tracking algorithms and their thresholds to immersive virtual reality frameworks needs to be validated. This study presents the development of a dispersion-threshold identification algorithm applied to data obtained from an eye-tracking system integrated into a head-mounted display. Rules-based criteria are proposed to calibrate the thresholds of the algorithm through different features, such as number of fixations and the percentage of points which belong to a fixation. The results show that distance-dispersion thresholds between 1–1.6° and time windows between

0.25 – 0.4

s are the acceptable range parameters, with 1° and

0.25

s being the optimum. The work presents a calibrated algorithm to be applied in future experiments with eye-tracking integrated into head-mounted displays and guidelines for calibrating fixation identification algorithms Full article

(This article belongs to the Special Issue Wearable Technologies and Applications for Eye Tracking)

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