Directional Gaze Analysis in Webcam Video Sequences

Vivero, V.; Barreira, N.; Penedo, Manuel G.; Cabrero, David; Remeseiro, Beatriz

doi:10.1007/978-3-642-13775-4_32

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…Another—somewhat obvious—limitation of this method is the requirement of human scorers to code subject eye movements. Automation of eye tracking using web camera data is a growing area of research, and open source programs exist for the automation of online, real-time eye tracking (Wang and Sung, 2001 ; Hansen and Pece, 2005 ; Li and Parkhurst, 2006 ; Pedersen and Spivey, 2006 ; Vivero et al, 2010 ; Krafka et al, 2016 ). However, the accuracy of current automated systems is below those of standard high frame rate eye tracking cameras.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

“…The quality and cost of these devices has allowed for their increased use worldwide and are now a standard feature on most smart devices, including desktop and laptop computers, tablets, and smart phones. Because eye movements can often provide a noninvasive “window on the brain,” the recording of eye movements using web cameras is a burgeoning area of research including both online and offline system development (Wang and Sung, 2001 ; Hansen and Pece, 2005 ; Vivero et al, 2010 ; Anderson et al, 2011 ; Lin et al, 2013 ; Petridis et al, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

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Web Camera Based Eye Tracking to Assess Visual Memory on a Visual Paired Comparison Task

et al. 2017

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Background: Web cameras are increasingly part of the standard hardware of most smart devices. Eye movements can often provide a noninvasive “window on the brain,” and the recording of eye movements using web cameras is a burgeoning area of research.Objective: This study investigated a novel methodology for administering a visual paired comparison (VPC) decisional task using a web camera.To further assess this method, we examined the correlation between a standard eye-tracking camera automated scoring procedure [obtaining images at 60 frames per second (FPS)] and a manually scored procedure using a built-in laptop web camera (obtaining images at 3 FPS).Methods: This was an observational study of 54 clinically normal older adults.Subjects completed three in-clinic visits with simultaneous recording of eye movements on a VPC decision task by a standard eye tracker camera and a built-in laptop-based web camera. Inter-rater reliability was analyzed using Siegel and Castellan's kappa formula. Pearson correlations were used to investigate the correlation between VPC performance using a standard eye tracker camera and a built-in web camera.Results: Strong associations were observed on VPC mean novelty preference score between the 60 FPS eye tracker and 3 FPS built-in web camera at each of the three visits (r = 0.88–0.92). Inter-rater agreement of web camera scoring at each time point was high (κ = 0.81–0.88). There were strong relationships on VPC mean novelty preference score between 10, 5, and 3 FPS training sets (r = 0.88–0.94). Significantly fewer data quality issues were encountered using the built-in web camera.Conclusions: Human scoring of a VPC decisional task using a built-in laptop web camera correlated strongly with automated scoring of the same task using a standard high frame rate eye tracker camera.While this method is not suitable for eye tracking paradigms requiring the collection and analysis of fine-grained metrics, such as fixation points, built-in web cameras are a standard feature of most smart devices (e.g., laptops, tablets, smart phones) and can be effectively employed to track eye movements on decisional tasks with high accuracy and minimal cost.

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Section: Limitations and Future Directionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Web Camera Based Eye Tracking to Assess Visual Memory on a Visual Paired Comparison Task

et al. 2017

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“…With web cameras ubiquitously available on most standard computing/smart devices, this represents a more scalable method to gather population-level and longitudinal data on cognitive health. Since eye movements provide a non-invasive "window to the brain" and offer valuable insights into cognitive function, the recording of gaze with web cameras has become a trend in recent cognitive research (Hansen and Pece 2005;Lin et al 2013;Petridis et al 2013;Vivero et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Validation of a digitally delivered visual paired comparison task: reliability and convergent validity with established cognitive tests

et al. 2019

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Alzheimer's disease (AD) affects the memory and cognitive function of approximately 5.7 million Americans. Early detection subsequently allows for earlier treatment and improves outcomes. Currently, there exists a validated 30-min eye-tracking cognitive assessment (VPC-30) for predicting AD risk. However, a shorter assessment would improve user experience and improve scalability. Thus, the purposes were to (1) determine convergent validity between the 5-min web camera-based eyetracking task (VPC-5) and VPC-30, (2) examine the relationship between VPC-5 and gold-standard cognitive tests, and (3) determine the reliability and stability of VPC-5. This prospective study included two healthy cohorts: older adults (65+ years, n = 20) and younger adults (18-46 years, n = 24). Participants were tested on two separate occasions. Visit 1 included the Montreal Cognitive Assessment (Mo-CA), Digit Symbol Coding test (DSC), NIH Toolbox Cognitive Battery (NIHTB-CB), VPC-30, and VPC-5. Visit 2 occurred at least 14 days later; participants completed the VPC-5, DSC, NIHTB-CB, and dual-task walking assessments (DT). VPC-30 significantly correlated with VPC-5 at the first (p < .001) and second (p = .001) time points. VPC-5 and DSC (p < .01) and Pattern Comparison Processing Speed Test (PSPAC) (p = .01) were also correlated on day 1. Significant associations existed between VPC-5 and DSC (p < .001), Flanker Inhibitory Control Test (p = .05), PSPAC (p < .001), and Picture Sequence Memory Test (p = .02) during day 14 testing session. The test retest reliability of VPC-5 was significant (p < .001). VPC-5 displayed moderate convergent validity with the VPC-30 and gold-standard measures of cognition, while demonstrating strong stability, suggesting it is a valuable assessment for monitoring memory function.

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“…Research on methods for the recording and analyzing eye movements from device-embedded web cameras continues to grow [37][38][39][40][41][42][43][44], demonstrating the utility of real-time online systems and offline recording systems. Perhaps the most significant advantage of built-in web cameras is their lack of geographical restriction to collect eye feature data on large samples sizes.…”

Section: Discussionmentioning

confidence: 99%

Factors Affecting Users' Behavioral Intention, Behavior and Therapy Outcome of a Mobile-Phone-Delivered Cognitive Behavioral Therapy for Insomnia (Preprint)

Fitrianie¹,

Horsch²,

Lancee³

et al. 2018

Preprint

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BACKGROUND A mobile app could be a powerful medium for providing individual support for cognitive behavioral therapy (CBT), as well as to facilitate the therapy adherence. Many studies have reported about the efficacy of such apps for insomnia treatment. However, little is known about factors that may explain the acceptance and uptake of such applications. OBJECTIVE This study, therefore examines factors that may explain variation between people's behavioral intention to use a CBT for insomnia (CBT-I) app and their use behavior, and the influence of the behavior on therapy outcomes. METHODS From literature, related factors were identified. Data were gathered from a field trial involving people with relatively mild insomnia using a CBT-I app. Applying the Partial Least Squares-Structural Equation Modeling method, the study examines a three-tier model. The first tier explored seven aspects of behavioral intention: performance expectancy, effort expectancy, social influence, self-efficacy, trust, affect, and anxiety. The second tier investigated the influence of behavioral intention and facilitating condition on user behavior, which was specifically defined as engaging with the app (i.e. sleep diaries and conversations with the app), doing relaxation exercises, and following sleep restriction exercises. Here, the relationship between app engagement and the other two exercise behaviors was also examined. Finally, the third tier tested the influence of the behavior on therapy outcomes: insight into own sleep pattern, general sleep knowledge, and insomnia severity. The latter was measured using Insomnia Severity Index. RESULTS Performance expectancy, effort expectancy, social influence, self-efficacy, and trust all explained part of the variation in behavioral intention, but not beyond the explanation provided by affect, which accounted for R2 = .59 (n = 89). Behavioral intention could explain two behavior factors, i.e. app engagement (R2 = .30, n = 89) and relaxation exercise (R2 = .42, n = 89). Engagement with the app was a determinant for the other behaviors, i.e. relaxation exercise (R2 = .42, n = 89) and sleep restriction (R2 = .54, n = 47). Furthermore, app engagement was the only determinant for one of the therapy outcomes, i.e. understanding own sleep pattern (R2 = .09, n = 72). We did not find an association between user behavior and insomnia severity. CONCLUSIONS We anticipate that the findings will help researchers and developers to focus on: (1) users' positive feelings about the app, as this was an indicator of their acceptance of the mobile app; and on (2) therapeutic activities delivered via the app, as this correlated with their sleep pattern awareness and their involvement in the exercises recommended by the CBT-I app. CLINICALTRIAL Netherlands Trial Register: NTR5560; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=5560 (archived by WebCite at http://www.webcitation.org/6noLaUdJ4)

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Directional Gaze Analysis in Webcam Video Sequences

Cited by 4 publications

References 9 publications

Web Camera Based Eye Tracking to Assess Visual Memory on a Visual Paired Comparison Task

Web Camera Based Eye Tracking to Assess Visual Memory on a Visual Paired Comparison Task

Validation of a digitally delivered visual paired comparison task: reliability and convergent validity with established cognitive tests

Factors Affecting Users' Behavioral Intention, Behavior and Therapy Outcome of a Mobile-Phone-Delivered Cognitive Behavioral Therapy for Insomnia (Preprint)

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