Lévy Flight Model of Gaze Trajectories to Assist in ADHD Diagnoses

Papanikolaou, Christos; Sharma, Akriti; Lind, Pedro G.; Lencastre, Pedro

doi:10.3390/e26050392

Cited by 1 publication

(1 citation statement)

References 52 publications

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“…This might become increasingly relevant in the near future, as methods of tracking the eyes using regular computer/phone cameras are being developed [29]. Furthermore, besides its accuracy and the need for fewer data, this metric is not dependent on definitions of fixations and saccades, which is still a matter of debate among researchers, and for which several competing algorithms exist [30,31]. The Λ metric is simple to conceptualize and implement, and it offers a clear, explainable approach in contrast to the often opaque AI-driven methods [32].…”

Section: Discussionmentioning

confidence: 99%

Identifying Autism Gaze Patterns in Five-Second Data Records

Lencastre,

Lotfigolian,

Lind

2024

Diagnostics

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One of the most challenging problems when diagnosing autism spectrum disorder (ASD) is the need for long sets of data. Collecting data during such long periods is challenging, particularly when dealing with children. This challenge motivates the investigation of possible classifiers of ASD that do not need such long data sets. In this paper, we use eye-tracking data sets covering only 5 s and introduce one metric able to distinguish between ASD and typically developed (TD) gaze patterns based on such short time-series and compare it with two benchmarks, one using the traditional eye-tracking metrics and one state-of-the-art AI classifier. Although the data can only track possible disorders in visual attention and our approach is not a substitute to medical diagnosis, we find that our newly introduced metric can achieve an accuracy of 93% in classifying eye gaze trajectories from children with ASD surpassing both benchmarks while needing fewer data. The classification accuracy of our method, using a 5 s data series, performs better than the standard metrics in eye-tracking and is at the level of the best AI benchmarks, even when these are trained with longer time series. We also discuss the advantages and limitations of our method in comparison with the state of the art: besides needing a low amount of data, this method is a simple, understandable, and straightforward criterion to apply, which often contrasts with “black box” AI methods.

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