Eye Tracking and KNN Based Detection of Schizophrenia

Kačur, Juraj; Polec, Jaroslav; Csoka, Filip

doi:10.1109/elmar.2019.8918840

Cited by 8 publications

(3 citation statements)

References 5 publications

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“…Recently, eye-tracking technology is emerging in the detection of diagnosis of medical purposes. In a study by Kacur et al (2019), the authors presented a method for the detection of schizophrenia disorders using gaze position with the Rorschach Inkblot Test. In a study by Yoo et al (2019), a gazewriting classification technique for numeric gaze-written entry is proposed.…”

Section: Pupil Positionmentioning

confidence: 99%

Eye-Tracking Feature Extraction for Biometric Machine Learning

2022

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ContextEye tracking is a technology to measure and determine the eye movements and eye positions of an individual. The eye data can be collected and recorded using an eye tracker. Eye-tracking data offer unprecedented insights into human actions and environments, digitizing how people communicate with computers, and providing novel opportunities to conduct passive biometric-based classification such as emotion prediction. The objective of this article is to review what specific machine learning features can be obtained from eye-tracking data for the classification task.MethodsWe performed a systematic literature review (SLR) covering the eye-tracking studies in classification published from 2016 to the present. In the search process, we used four independent electronic databases which were the IEEE Xplore, the ACM Digital Library, and the ScienceDirect repositories as well as the Google Scholar. The selection process was performed by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) search strategy. We followed the processes indicated in the PRISMA to choose the appropriate relevant articles.ResultsOut of the initial 420 articles that were returned from our initial search query, 37 articles were finally identified and used in the qualitative synthesis, which were deemed to be directly relevant to our research question based on our methodology.ConclusionThe features that could be extracted from eye-tracking data included pupil size, saccade, fixations, velocity, blink, pupil position, electrooculogram (EOG), and gaze point. Fixation was the most commonly used feature among the studies found.

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Section: Pupil Positionmentioning

confidence: 99%

Eye-Tracking Feature Extraction for Biometric Machine Learning

2022

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“…These approaches consider fixations as random variables generated by stochastic processes [9]. Markov chain allows to transform unequal sequences of fixations in fixed length vectors of probabilities and has been successfully applied to understand which features of an image gain more attention from users [11] as well as to compare viewing behavior of images between different groups of participants [12]. To our knowledge, probabilistic methods such as Markov chain have never been used to evaluate web usability of websites of public or educational institutions.…”

Section: Introductionmentioning

confidence: 99%

Markov chain to analyze web usability of a university website using eye tracking data

Zammarchi

Frigau

Molà

2021

Statistical Analysis

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Web usability is a crucial feature of a website, allowing users to easily find information in a short time. Eye tracking data registered during the execution of tasks allow to measure web usability in a more objective way compared to questionnaires. In this work, we evaluated the web usability of the website of the University of Cagliari through the analysis of eye tracking data with qualitative and quantitative methods. Performances of two groups of students (i.e., high school and university students) across 10 different tasks were compared in terms of time to completion, number of fixations and difficulty ratio. Transitions between different areas of interest (AOI) were analyzed in the two groups using Markov chain. For the majority of tasks, we did not observe significant differences in the performances of the two groups, suggesting that the information needed to complete the tasks could easily be retrieved by students with little previous experience in using the website. For a specific task, high school students showed a worse performance based on the number of fixations and a different Markov chain stationary distribution compared to university students. These results allowed to highlight elements of the pages that can be modified to improve web usability.

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“…K-nearest neighbor (kNN) classifiers have been widely employed to make classification out of eye-tracking and gazing data. Zhang et al [34], Kacur et al [35] used a kNN classifier to diagnose schizophrenia disorders using an eye-tracking system during Rorschach Inkblot Test [36].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach for the Automatic Estimation of Fixation-Time Data Signals’ Quality

Gabrieli

Balagtas

Esposito

et al. 2020

Sensors

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Fixation time measures have been widely adopted in studies with infants and young children because they can successfully tap on their meaningful nonverbal behaviors. While recording preverbal children’s behavior is relatively simple, analysis of collected signals requires extensive manual preprocessing. In this paper, we investigate the possibility of using different Machine Learning (ML)—a Linear SVC, a Non-Linear SVC, and K-Neighbors—classifiers to automatically discriminate between Usable and Unusable eye fixation recordings. Results of our models show an accuracy of up to the 80%, suggesting that ML tools can help human researchers during the preprocessing and labelling phase of collected data.

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Eye Tracking and KNN Based Detection of Schizophrenia

Cited by 8 publications

References 5 publications

Eye-Tracking Feature Extraction for Biometric Machine Learning

Eye-Tracking Feature Extraction for Biometric Machine Learning

Markov chain to analyze web usability of a university website using eye tracking data

A Machine Learning Approach for the Automatic Estimation of Fixation-Time Data Signals’ Quality

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