scholarly journals MLGaze: Machine Learning-Based Analysis of Gaze Error Patterns in Consumer Eye Tracking Systems

Vision ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 25
Author(s):  
Anuradha Kar
Keyword(s):  
Machine Learning ◽  
Eye Tracking ◽  
Data Quality ◽  
Regression Models ◽  
Operating Conditions ◽  
Eye Tracker ◽  
Error Sources ◽  
Error Patterns ◽  
Machine Learning Methods ◽  
The Impact

Analyzing the gaze accuracy characteristics of an eye tracker is a critical task as its gaze data is frequently affected by non-ideal operating conditions in various consumer eye tracking applications. In previous research on pattern analysis of gaze data, efforts were made to model human visual behaviors and cognitive processes. What remains relatively unexplored are questions related to identifying gaze error sources as well as quantifying and modeling their impacts on the data quality of eye trackers. In this study, gaze error patterns produced by a commercial eye tracking device were studied with the help of machine learning algorithms, such as classifiers and regression models. Gaze data were collected from a group of participants under multiple conditions that commonly affect eye trackers operating on desktop and handheld platforms. These conditions (referred here as error sources) include user distance, head pose, and eye-tracker pose variations, and the collected gaze data were used to train the classifier and regression models. It was seen that while the impact of the different error sources on gaze data characteristics were nearly impossible to distinguish by visual inspection or from data statistics, machine learning models were successful in identifying the impact of the different error sources and predicting the variability in gaze error levels due to these conditions. The objective of this study was to investigate the efficacy of machine learning methods towards the detection and prediction of gaze error patterns, which would enable an in-depth understanding of the data quality and reliability of eye trackers under unconstrained operating conditions. Coding resources for all the machine learning methods adopted in this study were included in an open repository named MLGaze to allow researchers to replicate the principles presented here using data from their own eye trackers.

scholarly journals Development of Open-source Software and Gaze Data Repositories for Performance Evaluation of Eye Tracking Systems

Vision ◽  
2019 ◽  
Vol 3 (4) ◽  
pp. 55
Author(s):  
Kar ◽  
Corcoran
Keyword(s):  
Eye Tracking ◽  
Data Quality ◽  
Open Source ◽  
Eye Gaze ◽  
Operating Conditions ◽  
Data Repository ◽  
Tracking Systems ◽  
Data Repositories ◽  
Multiple User ◽  
The Impact

In this paper, a range of open-source tools, datasets, and software that have been developed for quantitative and in-depth evaluation of eye gaze data quality are presented. Eye tracking systems in contemporary vision research and applications face major challenges due to variable operating conditions such as user distance, head pose, and movements of the eye tracker platform. However, there is a lack of open-source tools and datasets that could be used for quantitatively evaluating an eye tracker’s data quality, comparing performance of multiple trackers, or studying the impact of various operating conditions on a tracker’s accuracy. To address these issues, an open-source code repository named GazeVisual-Lib is developed that contains a number of algorithms, visualizations, and software tools for detailed and quantitative analysis of an eye tracker’s performance and data quality. In addition, a new labelled eye gaze dataset that is collected from multiple user platforms and operating conditions is presented in an open data repository for benchmark comparison of gaze data from different eye tracking systems. The paper presents the concept, development, and organization of these two repositories that are envisioned to improve the performance analysis and reliability of eye tracking systems.

scholarly journals The impact of slippage on the data quality of head-worn eye trackers

2020 ◽  
Vol 52 (3) ◽  
pp. 1140-1160 ◽  
Author(s):  
Diederick C. Niehorster ◽  
Thiago Santini ◽  
Roy S. Hessels ◽  
Ignace T. C. Hooge ◽  
Enkelejda Kasneci ◽  
...  
Keyword(s):  
Eye Tracking ◽  
Data Quality ◽  
Facial Expressions ◽  
Estimation Algorithm ◽  
Eye Tracker ◽  
Gaze Behavior ◽  
Face To Face ◽  
Movement Data ◽  
The Impact

AbstractMobile head-worn eye trackers allow researchers to record eye-movement data as participants freely move around and interact with their surroundings. However, participant behavior may cause the eye tracker to slip on the participant’s head, potentially strongly affecting data quality. To investigate how this eye-tracker slippage affects data quality, we designed experiments in which participants mimic behaviors that can cause a mobile eye tracker to move. Specifically, we investigated data quality when participants speak, make facial expressions, and move the eye tracker. Four head-worn eye-tracking setups were used: (i) Tobii Pro Glasses 2 in 50 Hz mode, (ii) SMI Eye Tracking Glasses 2.0 60 Hz, (iii) Pupil-Labs’ Pupil in 3D mode, and (iv) Pupil-Labs’ Pupil with the Grip gaze estimation algorithm as implemented in the EyeRecToo software. Our results show that whereas gaze estimates of the Tobii and Grip remained stable when the eye tracker moved, the other systems exhibited significant errors (0.8–3.1∘ increase in gaze deviation over baseline) even for the small amounts of glasses movement that occurred during the speech and facial expressions tasks. We conclude that some of the tested eye-tracking setups may not be suitable for investigating gaze behavior when high accuracy is required, such as during face-to-face interaction scenarios. We recommend that users of mobile head-worn eye trackers perform similar tests with their setups to become aware of its characteristics. This will enable researchers to design experiments that are robust to the limitations of their particular eye-tracking setup.

scholarly journals The impact of translation modality on user experience: an eye-tracking study of the Microsoft Word user interface

2021 ◽  
Author(s):  
Ana Guerberof Arenas ◽  
Joss Moorkens ◽  
Sharon O’Brien
Keyword(s):  
User Interface ◽  
Eye Tracking ◽  
Cognitive Load ◽  
Task Completion ◽  
Eye Tracker ◽  
Software Products ◽  
Microsoft Word ◽  
Set Up ◽  
The Impact ◽  
The Given

AbstractThis paper presents results of the effect of different translation modalities on users when working with the Microsoft Word user interface. An experimental study was set up with 84 Japanese, German, Spanish, and English native speakers working with Microsoft Word in three modalities: the published translated version, a machine translated (MT) version (with unedited MT strings incorporated into the MS Word interface) and the published English version. An eye-tracker measured the cognitive load and usability according to the ISO/TR 16982 guidelines: i.e., effectiveness, efficiency, and satisfaction followed by retrospective think-aloud protocol. The results show that the users’ effectiveness (number of tasks completed) does not significantly differ due to the translation modality. However, their efficiency (time for task completion) and self-reported satisfaction are significantly higher when working with the released product as opposed to the unedited MT version, especially when participants are less experienced. The eye-tracking results show that users experience a higher cognitive load when working with MT and with the human-translated versions as opposed to the English original. The results suggest that language and translation modality play a significant role in the usability of software products whether users complete the given tasks or not and even if they are unaware that MT was used to translate the interface.

scholarly journals Machine Learning Methods Applied for Modeling the Process of Obtaining Bricks Using Silicon-Based Materials

Materials ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7232
Author(s):  
Costel Anton ◽  
Silvia Curteanu ◽  
Cătălin Lisa ◽  
Florin Leon
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Raw Materials ◽  
Optimization Procedure ◽  
Exhaust Emission ◽  
Energy Potential ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Emission Changes ◽  
The Impact

Most of the time, industrial brick manufacture facilities are designed and commissioned for a particular type of manufacture mix and a particular type of burning process. Productivity and product quality maintenance and improvement is a challenge for process engineers. Our paper aims at using machine learning methods to evaluate the impact of adding new auxiliary materials on the amount of exhaust emissions. Experimental determinations made in similar conditions enabled us to build a database containing information about 121 brick batches. Various models (artificial neural networks and regression algorithms) were designed to make predictions about exhaust emission changes when auxiliary materials are introduced into the manufacture mix. The best models were feed-forward neural networks with two hidden layers, having MSE < 0.01 and r2 > 0.82 and, as regression model, kNN with error < 0.6. Also, an optimization procedure, including the best models, was developed in order to determine the optimal values for the parameters that assure the minimum quantities for the gas emission. The Pareto front obtained in the multi-objective optimization conducted with grid search method allows the user the chose the most convenient values for the dry product mass, clay, ash and organic raw materials which minimize gas emissions with energy potential.

scholarly journals Machine learning methods for soil moisture prediction in vineyards using digital images

2020 ◽  
Vol 167 ◽  
pp. 02004
Author(s):  
Chantal Saad Hajjar ◽  
Celine Hajjar ◽  
Michel Esta ◽  
Yolla Ghorra Chamoun
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Regression Models ◽  
Digital Images ◽  
Pearson Correlation ◽  
Support Vector ◽  
Learning Methods ◽  
Nonlinear Regression Models ◽  
Machine Learning Methods ◽  
The One

In this paper, we propose to estimate the moisture of vineyard soils from digital photography using machine learning methods. Two nonlinear regression models are implemented: a multilayer perceptron (MLP) and a support vector regression (SVR). Pixels coded with RGB colour model extracted from soil digital images along with the associated known soil moisture levels are used to train both models in order to predict moisture content from newly acquired images. The study is conducted on samples of six soil types collected from Chateau Kefraya terroirs in Lebanon. Both methods succeeded in forecasting moisture giving high correlation values between the measured moisture and the predicted moisture when tested on unknown data. However, the method based on SVR outperformed the one based on MLP yielding Pearson correlation coefficient values ranging from 0.89 to 0.99. Moreover, it is a simple and noninvasive method that can be adopted easily to detect vineyards soil moisture.

scholarly journals Computer-generated facial areas of interest in eye tracking research: A simulation study

2021 ◽  
Author(s):  
Antonia Vehlen ◽  
William Standard ◽  
Gregor Domes
Keyword(s):  
Eye Tracking ◽  
Voronoi Tessellation ◽  
Data Accuracy ◽  
Type I ◽  
Type Ii ◽  
Eye Tracker ◽  
Type I Errors ◽  
Areas Of Interest ◽  
Type Ii Errors ◽  
The Impact

Advances in eye tracking technology have enabled the development of interactive experimental setups to study social attention. Since these setups differ substantially from the eye tracker manufacturer’s test conditions, validation is essential with regard to data quality and other factors potentially threatening data validity. In this study, we evaluated the impact of data accuracy and areas of interest (AOIs) size on the classification of simulated gaze data. We defined AOIs of different sizes using the Limited-Radius Voronoi-Tessellation (LRVT) method, and simulated gaze data for facial target points with varying data accuracy. As hypothesized, we found that data accuracy and AOI size had strong effects on gaze classification. In addition, these effects were not independent and differed for falsely classified gaze inside AOIs (Type I errors) and falsely classified gaze outside the predefined AOIs (Type II errors). The results indicate that smaller AOIs generally minimize false classifications as long as data accuracy is good enough. For studies with lower data accuracy, Type II errors can still be compensated to some extent by using larger AOIs, but at the cost of an increased probability of Type I errors. Proper estimation of data accuracy is therefore essential for making informed decisions regarding the size of AOIs.

scholarly journals Quantified uncertainties in fission yields from machine learning

2020 ◽  
Vol 242 ◽  
pp. 05003
Author(s):  
A.E. Lovell ◽  
A.T. Mohan ◽  
P. Talou ◽  
M. Chertkov
Keyword(s):  
Machine Learning ◽  
Nuclear Physics ◽  
Experimental Error ◽  
Training Data ◽  
Learning Methods ◽  
Mixture Density ◽  
Machine Learning Methods ◽  
Predicted Values ◽  
The Impact ◽  
Fission Yields

As machine learning methods gain traction in the nuclear physics community, especially those methods that aim to propagate uncertainties to unmeasured quantities, it is important to understand how the uncertainty in the training data coming either from theory or experiment propagates to the uncertainty in the predicted values. Gaussian Processes and Bayesian Neural Networks are being more and more widely used, in particular to extrapolate beyond measured data. However, studies are typically not performed on the impact of the experimental errors on these extrapolated values. In this work, we focus on understanding how uncertainties propagate from input to prediction when using machine learning methods. We use a Mixture Density Network (MDN) to incorporate experimental error into the training of the network and construct uncertainties for the associated predicted quantities. Systematically, we study the effect of the size of the experimental error, both on the reproduced training data and extrapolated predictions for fission yields of actinides.

scholarly journals Gaze and Event Tracking for Evaluation of Recommendation-Driven Purchase

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1381 ◽  
Author(s):  
Piotr Sulikowski ◽  
Tomasz Zdziebko ◽  
Kristof Coussement ◽  
Krzysztof Dyczkowski ◽  
Krzysztof Kluza ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Eye Tracking ◽  
User Behavior ◽  
Implicit Feedback ◽  
Physical Parameters ◽  
Object Model ◽  
Machine Learning Methods ◽  
Event Tracking ◽  
Item Presentation

Recommendation systems play an important role in e-commerce turnover by presenting personalized recommendations. Due to the vast amount of marketing content online, users are less susceptible to these suggestions. In addition to the accuracy of a recommendation, its presentation, layout, and other visual aspects can improve its effectiveness. This study evaluates the visual aspects of recommender interfaces. Vertical and horizontal recommendation layouts are tested, along with different visual intensity levels of item presentation, and conclusions obtained with a number of popular machine learning methods are discussed. Results from the implicit feedback study of the effectiveness of recommending interfaces for four major e-commerce websites are presented. Two different methods of observing user behavior were used, i.e., eye-tracking and document object model (DOM) implicit event tracking in the browser, which allowed collecting a large amount of data related to user activity and physical parameters of recommending interfaces. Results have been analyzed in order to compare the reliability and applicability of both methods. Observations made with eye tracking and event tracking led to similar results regarding recommendation interface evaluation. In general, vertical interfaces showed higher effectiveness compared to horizontal ones, with the first and second positions working best, and the worse performance of horizontal interfaces probably being connected with banner blindness. Neural networks provided the best modeling results of the recommendation-driven purchase (RDP) phenomenon.

scholarly journals The Effects of Digitally Mediated Multimodal Indirect Feedback on Narrations in L2 Spanish Writing: Eye Tracking as a Measure of Noticing

Languages ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 159
Author(s):  
Laura E. Valentin-Rivera ◽  
Li Yang
Keyword(s):  
Eye Tracking ◽  
Corrective Feedback ◽  
Target Language ◽  
Eye Tracker ◽  
Revision Process ◽  
Video Tutorial ◽  
Written Corrective Feedback ◽  
L2 Development ◽  
Indirect Feedback ◽  
The Impact

Written corrective feedback (CF) could pave the way for L2 development, especially when embedded in multimodality. Building on prior research, this descriptive study drew a relationship between specific types of errors that were most successfully revised and noticing measured by eye-tracking techniques. Additionally, this study furthers our understanding of the impact of indirect CF (i.e., codes accompanied by metalinguistic hints) delivered by two multimodal components: (a) a video tutorial on how to approach teachers’ comments and (b) a soundless video displaying individualized teacher feedback. To this end, three L2 learners of Spanish completed a narration in the target language, watched a tutorial on attending to CF, received indirect feedback via the personalized soundless video (i.e., option “b” above), and corrected their errors. An eye tracker recorded all ocular activity while the participants watched both recordings. The results suggested that receiving training on approaching teachers’ comments may enhance the overall success rate of revisions, especially in verb and vocabulary-related errors. Last, a detailed unfolding of the revision process unveiled by eye-tracking data accounted for (1) an explanation of why two specific types of errors were more successfully revised and (2) some pedagogical recommendations.

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