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

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.

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Evaluation of an eye tracking setup for studying visual attention in face-to-face conversations

Scientific Reports ◽

10.1038/s41598-021-81987-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Antonia Vehlen ◽

Ines Spenthof ◽

Daniel Tönsing ◽

Markus Heinrichs ◽

Gregor Domes

Keyword(s):

Eye Tracking ◽

Data Quality ◽

Autism Spectrum ◽

Independent Study ◽

Facial Features ◽

Gaze Behavior ◽

Tracking Accuracy ◽

Attentional Processing ◽

Face To Face ◽

Gaze Patterns

AbstractMany eye tracking studies use facial stimuli presented on a display to investigate attentional processing of social stimuli. To introduce a more realistic approach that allows interaction between two real people, we evaluated a new eye tracking setup in three independent studies in terms of data quality, short-term reliability and feasibility. Study 1 measured the robustness, precision and accuracy for calibration stimuli compared to a classical display-based setup. Study 2 used the identical measures with an independent study sample to compare the data quality for a photograph of a face (2D) and the face of the real person (3D). Study 3 evaluated data quality over the course of a real face-to-face conversation and examined the gaze behavior on the facial features of the conversation partner. Study 1 provides evidence that quality indices for the scene-based setup were comparable to those of a classical display-based setup. Average accuracy was better than 0.4° visual angle. Study 2 demonstrates that eye tracking quality is sufficient for 3D stimuli and robust against short interruptions without re-calibration. Study 3 confirms the long-term stability of tracking accuracy during a face-to-face interaction and demonstrates typical gaze patterns for facial features. Thus, the eye tracking setup presented here seems feasible for studying gaze behavior in dyadic face-to-face interactions. Eye tracking data obtained with this setup achieves an accuracy that is sufficient for investigating behavior such as eye contact in social interactions in a range of populations including clinical conditions, such as autism spectrum and social phobia.

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

Vision ◽

10.3390/vision4020025 ◽

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.

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Conducting Population Health Research during the COVID-19 Pandemic: Impacts and Recommendations

Sustainability ◽

10.3390/su13063320 ◽

2021 ◽

Vol 13 (6) ◽

pp. 3320

Author(s):

Amy R. Villarosa ◽

Lucie M. Ramjan ◽

Della Maneze ◽

Ajesh George

Keyword(s):

Data Collection ◽

Health Services ◽

Data Quality ◽

Population Health ◽

Health Research ◽

Alternative Methods ◽

Residential Aged Care ◽

Recruitment Methods ◽

Face To Face ◽

The Impact

The COVID-19 pandemic has resulted in many changes, including restrictions on indoor gatherings and visitation to residential aged care facilities, hospitals and certain communities. Coupled with potential restrictions imposed by health services and academic institutions, these changes may significantly impact the conduct of population health research. However, the continuance of population health research is beneficial for the provision of health services and sometimes imperative. This paper discusses the impact of COVID-19 restrictions on the conduct of population health research. This discussion unveils important ethical considerations, as well as potential impacts on recruitment methods, face-to-face data collection, data quality and validity. In addition, this paper explores potential recruitment and data collection methods that could replace face-to-face methods. The discussion is accompanied by reflections on the challenges experienced by the authors in their own research at an oral health service during the COVID-19 pandemic and alternative methods that were utilised in place of face-to-face methods. This paper concludes that, although COVID-19 presents challenges to the conduct of population health research, there is a range of alternative methods to face-to-face recruitment and data collection. These alternative methods should be considered in light of project aims to ensure data quality is not compromised.

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

Machine Translation ◽

10.1007/s10590-021-09267-z ◽

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.

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The Impact of Between-Wave Engagement Strategies on Response to a Longitudinal Survey

International Journal of Market Research ◽

10.2501/ijmr-2015-046 ◽

2015 ◽

Vol 57 (4) ◽

pp. 533-554 ◽

Cited By ~ 1

Author(s):

Andrew Cleary ◽

Nigel Balmer

Keyword(s):

Longitudinal Survey ◽

Longitudinal Surveys ◽

Face To Face ◽

Participant Engagement ◽

Engagement Strategies ◽

Design Attributes ◽

New Research ◽

The Impact ◽

Better Than

Maintaining participant engagement in longitudinal surveys has been a key focus of survey research, and has implications for the quality of response and cost of administration. This paper presents new research measuring the impact of the design of between-wave keeping-in-touch mailings on response to the mailing and subsequent wave of a longitudinal survey. Three design attributes of the mailings were randomly implemented: the form of response request (whether respondents were asked to respond only if their address had changed, or in all cases to confirm or update their address); the newsletter included with the mailing (contrasting a newsletter with content tailored to respondent characteristics with a general newsletter and no newsletter); and the outgoing postage used (stamped or franked). The experiments were fielded on a new longitudinal study, the English and Welsh Civil and Social Justice Panel Survey (CSJPS), and took place between waves one and two. Fieldwork for both waves was conducted by Ipsos MORI face-to-face interviewers. Our main finding was that the tailored newsletter was associated with a significant increase in the wave-two response rate. However, in relation to response to the request, the tailored newsletter, or sending no newsletter at all, were equally effective at inducing response, and significantly better than the general newsletter. We also found that, in relation to the form of request, the ‘change of address’ request was as effective as the more costly ‘confirmation’ request. Findings are discussed with reference to the design of keeping-in-touch mailings for longitudinal surveys.

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Use of machine learning to identify autism with natural social gaze behavior (Preprint)

10.2196/preprints.29328 ◽

2021 ◽

Author(s):

Zhong Zhao ◽

Haiming Tang ◽

Xiaobin Zhang ◽

Xingda Qu ◽

Jianping Lu

Keyword(s):

Machine Learning ◽

Eye Tracking ◽

Classification Accuracy ◽

Autism Spectrum ◽

Support Vector ◽

Gaze Behavior ◽

Linear Discriminant ◽

Face To Face ◽

Area Of Interest ◽

Social Gaze

BACKGROUND Abnormal gaze behavior is a prominent feature of the autism spectrum disorder (ASD). Previous eye tracking studies had participants watch images (i.e., picture, video and webpage), and the application of machine learning (ML) on these data showed promising results in identify ASD individuals. Given the fact that gaze behavior differs in face-to-face interaction from image viewing tasks, no study has investigated whether natural social gaze behavior could accurately identify ASD. OBJECTIVE The objective of this study was to examine whether and what area of interest (AOI)-based features extracted from the natural social gaze behavior could identify ASD. METHODS Both children with ASD and typical development (TD) were eye-tracked when they were engaged in a face-to-face conversation with an interviewer. Four ML classifiers (support vector machine, SVM; linear discriminant analysis, LDA; decision tree, DT; and random forest, RF) were used to determine the maximum classification accuracy and the corresponding features. RESULTS A maximum classification accuracy of 84.62% were achieved with three classifiers (LDA, DT and RF). Results showed that the mouth, but not the eyes AOI, was a powerful feature in detecting ASD. CONCLUSIONS Natural gaze behavior could be leveraged to identify ASD, suggesting that ASD might be objectively screened with eye tracking technology in everyday social interaction. In addition, the comparison between our and previous findings suggests that eye tracking features that could identify ASD might be culture dependent and context sensitive.

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Videoconferencing Psychotherapy: a literature review of the perspective of relationships for patients and psychologists (Preprint)

10.2196/preprints.19004 ◽

2020 ◽

Author(s):

Francesco Cataldo ◽

Shanton Chang ◽

Antonette Mendoza ◽

George Buchanan

Keyword(s):

Literature Review ◽

Therapeutic Alliance ◽

Therapeutic Relationship ◽

Active Role ◽

High Demand ◽

Face To Face ◽

Patient Relationships ◽

The Impact ◽

The Relationship

BACKGROUND During the COVID-19 pandemic, people are being encouraged to maintain social distance. Technology is helping people to reschedule meetings from “face-to-face” interactions to remote videoconferencing. Psychologists are in high demand, due to an increase in stress as a result of COVID. Many seek to both keep treating their current patients, and welcome new ones, given the current high demand for their services. Videoconferencing provides an opportunity to do this. However, shifting treatment from face-to-face to the videoconferencing is not simple as both the psychologist and the patient miss the in-person information and cues, such as body language provides. OBJECTIVE A new theoretical framework is proposed to guide the design of future studies on the impact of the computer as a mediator of psychologist-patient relationships, and the influence of videoconferencing on the whole relationship process. METHODS A literature review has been conducted, screening studies focusing on communication, and the key concepts of therapeutic relationship and therapeutic alliance. RESULTS Studies report that patients are generally satisfied with videoconference therapy in terms of the relationship with their therapists and the establishment of the “therapeutic alliance”. However, psychologists report difficulties in establishing same quality of therapeutic relationship and therapeutic alliance. The analysed studies lead us to interpret data under a different perspective. A new model of relationship is proposed, along with further hypotheses. CONCLUSIONS It is important to consider the computer as having an active role in psychologists and patients’ relationships. CLINICALTRIAL

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Developmental Changes in Gaze Behavior and the Effects of Auditory Emotion Word Priming in Emotional Face Categorization

Multisensory Research ◽

10.1163/22134808-bja10063 ◽

2021 ◽

pp. 1-21

Author(s):

Michael Vesker ◽

Daniela Bahn ◽

Christina Kauschke ◽

Gudrun Schwarzer

Keyword(s):

Facial Expressions ◽

Response Times ◽

Age Groups ◽

Fixation Time ◽

Eye Tracker ◽

Gaze Behavior ◽

Older Children ◽

Emotion Word ◽

Visual Encoding ◽

Face Categorization

Abstract Social interactions often require the simultaneous processing of emotions from facial expressions and speech. However, the development of the gaze behavior used for emotion recognition, and the effects of speech perception on the visual encoding of facial expressions is less understood. We therefore conducted a word-primed face categorization experiment, where participants from multiple age groups (six-year-olds, 12-year-olds, and adults) categorized target facial expressions as positive or negative after priming with valence-congruent or -incongruent auditory emotion words, or no words at all. We recorded our participants’ gaze behavior during this task using an eye-tracker, and analyzed the data with respect to the fixation time toward the eyes and mouth regions of faces, as well as the time until participants made the first fixation within those regions (time to first fixation, TTFF). We found that the six-year-olds showed significantly higher accuracy in categorizing congruently primed faces compared to the other conditions. The six-year-olds also showed faster response times, shorter total fixation durations, and faster TTFF measures in all primed trials, regardless of congruency, as compared to unprimed trials. We also found that while adults looked first, and longer, at the eyes as compared to the mouth regions of target faces, children did not exhibit this gaze behavior. Our results thus indicate that young children are more sensitive than adults or older children to auditory emotion word primes during the perception of emotional faces, and that the distribution of gaze across the regions of the face changes significantly from childhood to adulthood.

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Ontology for Data Quality and Chronic Disease Management

Healthcare Informatics and Analytics - Advances in Healthcare Information Systems and Administration ◽

10.4018/978-1-4666-6316-9.ch016 ◽

2015 ◽

pp. 303-331

Author(s):

Alireza Rahimi ◽

Siaw-Teng Liaw ◽

Pradeep Kumar Ray ◽

Jane Taggart ◽

Hairong Yu

Keyword(s):

Chronic Disease ◽

Data Quality ◽

English Language ◽

Assessment Tools ◽

Fitness For Purpose ◽

Language Studies ◽

Full Text Review ◽

Automated Tools ◽

The Impact

Improved Data Quality (DQ) can improve the quality of decisions and lead to better policy in health organizations. Ontologies can support automated tools to assess DQ. This chapter examines ontology-based approaches to conceptualization and specification of DQ based on “fitness for purpose” within the health context. English language studies that addressed DQ, fitness for purpose, ontology-based approaches, and implementations were included. The authors screened 315 papers; excluded 36 duplicates, 182 on abstract review, and 46 on full-text review; leaving 52 papers. These were appraised with a realist “context-mechanism-impacts/outcomes” template. The authors found a lack of consensus frameworks or definitions for DQ and comprehensive ontological approaches to DQ or fitness for purpose. The majority of papers described the processes of the development of DQ tools. Some assessed the impact of implementing ontology-based specifications for DQ. There were few evaluative studies of the performance of DQ assessment tools developed; none compared ontological with non-ontological approaches.

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The Impact of Gaze Controlled Technology on Quality of Life

Gaze Interaction and Applications of Eye Tracking ◽

10.4018/978-1-61350-098-9.ch006 ◽

2012 ◽

pp. 48-54 ◽

Cited By ~ 2

Author(s):

Valentina Pasian ◽

Fulvio Corno ◽

Isabella Signorile ◽

Laura Farinetti

Keyword(s):

Quality Of Life ◽

Eye Tracking ◽

Progressive Disease ◽

Gaze Control ◽

Communication Method ◽

Tracking Device ◽

The Impact

This chapter presents the process of introducing an eye tracking device to impaired users. It reports results from a gaze control user trial conducted with people for whom gaze control is a necessity due to their current condition or for whom it will soon become a necessity because of a progressive disease. Special attention is paid to the impact of this new communication method on their quality of life.

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