Diagnosis of Autism Using an Eye Tracking System

International audienceAutism Spectrum Disorders(ASD) are characterized by a deficitin social integration, anguage development, and restricted interests. ASD is defined as a prevalence development life disability. However, children who are early diagnosed and intervention improve long-term prognosis.This project proposes to detect autism in children at a first level (preclinical stage) using a tool "eye tracking" highly cost-effective and mbedded in a tablet. The results obtained with thissystem have been compared with the outcomes of the Modified Autism in ChildrenM-CHAT questionnaire

Hybrid Learning in Times of Pandemic Covid-19: An Experience in a Lima University

Hybrid learning became more important in the context of the Covid-19 pandemic. In this sense, the University of Sciences and Humanities implemented adequate strategies to guarantee the continuity of the teaching and learning process. At the beginning, the virtual modality was chosen in its entirety and then moved to a hybrid modality; however, teachers were not trained; students had problems of connectivity and access to technological resources, which led more than 35% of them to abandon their studies. Therefore, the purpose of the study is to know the students' perception of the hybrid modality, through a questionnaire applied to 142 students of the 2021-1 cycle of the Faculty of Accounting, Economic and Financial Sciences, Faculty of Health Sciences, Faculty of Humanities and Social Sciences, Faculty of Engineering Sciences, considering 19 questions integrated in 4 dimensions: teaching strategy, didactic resources, evaluation system and tutoring; all this in order to evaluate the strategies of the hybrid model proposed by the university. The results obtained in the dimensions show the highest score in the evaluation dimension with an average of 4.51, specifically in the planning of the syllabus; however, the lowest average is found in the tutoring dimension with an average of 3.22, which corresponds to the participation in psycho-pedagogical workshops to improve academic performance. It is concluded that the teaching strategy in the hybrid modality has been satisfactorily approved by the students, thanks to the periodic training of teachers. However, tutoring should be strengthened through academic counseling, so that the beneficiaries of this research are the students, teachers and parents; and, therefore, the Peruvian educational system

Developing an eye-tracking algorithm as a potential tool for early diagnosis of autism spectrum disorder in children

<div><p>Background</p><p>Autism spectrum disorder (ASD) currently affects nearly 1 in 160 children worldwide. In over two-thirds of evaluations, no validated diagnostics are used and gold standard diagnostic tools are used in less than 5% of evaluations. Currently, the diagnosis of ASD requires lengthy and expensive tests, in addition to clinical confirmation. Therefore, fast, cheap, portable, and easy-to-administer screening instruments for ASD are required. Several studies have shown that children with ASD have a lower preference for social scenes compared with children without ASD. Based on this, eye-tracking and measurement of gaze preference for social scenes has been used as a screening tool for ASD. Currently available eye-tracking software requires intensive calibration, training, or holding of the head to prevent interference with gaze recognition limiting its use in children with ASD.</p><p>Methods</p><p>In this study, we designed a simple eye-tracking algorithm that does not require calibration or head holding, as a platform for future validation of a cost-effective ASD potential screening instrument. This system operates on a portable and inexpensive tablet to measure gaze preference of children for social compared to abstract scenes. A child watches a one-minute stimulus video composed of a social scene projected on the left side and an abstract scene projected on the right side of the tablet’s screen. We designed five stimulus videos by changing the social/abstract scenes. Every child observed all the five videos in random order. We developed an eye-tracking algorithm that calculates the child’s gaze preference for the social and abstract scenes, estimated as the percentage of the accumulated time that the child observes the left or right side of the screen, respectively. Twenty-three children without a prior history of ASD and 8 children with a clinical diagnosis of ASD were evaluated. The recorded video of the child´s eye movement was analyzed both manually by an observer and automatically by our algorithm.</p><p>Results</p><p>This study demonstrates that the algorithm correctly differentiates visual preference for either the left or right side of the screen (social or abstract scenes), identifies distractions, and maintains high accuracy compared to the manual classification. The error of the algorithm was 1.52%, when compared to the gold standard of manual observation.</p><p>Discussion</p><p>This tablet-based gaze preference/eye-tracking algorithm can estimate gaze preference in both children with ASD and without ASD to a high degree of accuracy, without the need for calibration, training, or restraint of the children. This system can be utilized in low-resource settings as a portable and cost-effective potential screening tool for ASD.</p></div

L'Auto-vélo : automobilisme, cyclisme, athlétisme, yachting, aérostation, escrime, hippisme / dir. Henri Desgranges

11 mars 19211921/03/11 (A22,N7391)

Block diagram of the eye-tracking tool illustrating the individual components.

<p>Block diagram of the eye-tracking tool illustrating the individual components.</p

Correlation / scatter plot between the manual and algorithm estimates for the percentages of time of gaze preference towards the abstract/social scenes and distractions.

<p>Correlation / scatter plot between the manual and algorithm estimates for the percentages of time of gaze preference towards the abstract/social scenes and distractions.</p

Graphical user interface of the eye-tracking tool, demonstrating the patient data input, the main controls for the video display and video capture, and visual preference data output.

<p>Demonstration of the frame-by-frame analysis for visual preference by displaying pupil position for both eyes, allowing for investigator corroboration.</p

Manual and automatic (performed by the algorithm) evaluation of the percentage of time preference towards the social and abstract scenes.

<p>No significant difference is observed between the two methods.</p