Evidence map›Paper›PMID 40281730›Full record

ArticleBioengineering (Basel, Switzerland)2025

The Feasibility of RGB-D Gaze Intention Measurement in Children with Autism Using Azure Kinect.

Abderrahmen Bendimered, Rim Cherif, Rabah Iguernaissi, Mohamad Motasem Nawaf, Susanne Thümmler, Séverine Dubuisson, Djamal Merad

Abstract read
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Abderrahmen BendimeredLaboratoire d'Informatique et des Systèmes, CNRS UMR 7020, Université de Marseille, Aix-Marseille University, 13009 Marseille, France.ORCID 0000-0003-3453-904X
Rim CherifLaboratoire d'Informatique et des Systèmes, CNRS UMR 7020, Université de Marseille, Aix-Marseille University, 13009 Marseille, France.ORCID 0009-0008-2260-6755
Rabah IguernaissiLaboratoire d'Informatique et des Systèmes, CNRS UMR 7020, Université de Marseille, Aix-Marseille University, 13009 Marseille, France.ORCID 0000-0002-1728-3532
Mohamad Motasem NawafLaboratoire d'Informatique et des Systèmes, CNRS UMR 7020, Université de Marseille, Aix-Marseille University, 13009 Marseille, France.ORCID 0000-0003-2156-8589
Susanne ThümmlerUniversity Department of Child and Adolescent Psychiatry, Children's Hospitals of Nice CHU Lenval, 06103 Nice, France.ORCID 0000-0001-9993-6981
Séverine DubuissonLaboratoire d'Informatique et des Systèmes, CNRS UMR 7020, Université de Marseille, Aix-Marseille University, 13009 Marseille, France.ORCID 0000-0001-7306-4134
Djamal MeradLaboratoire d'Informatique et des Systèmes, CNRS UMR 7020, Université de Marseille, Aix-Marseille University, 13009 Marseille, France.ORCID 0000-0001-9233-0020

Funding

National Research Agency (ANR) NR-19-CE19-0004.
6 · The paper itself

Abstract

Gaze interpretation is a fundamental aspect of social communication, especially for children with autism spectrum disorder (ASD), who frequently encounter many difficulties in social interactions. Despite the considerable advances made in gaze tracking technologies, such as those based on RGB and RGB-D, the accurate measurement of gaze direction remains a significant scientific challenge. This paper proposes a novel approach utilizing the Azure Kinect to improve the measurement of gaze intention in children with ASD, providing accurate estimations of both gaze direction and head position. To evaluate the effectiveness of the proposed methodology, an experimental trial was conducted with eight participants of varying statures. The intersection of the estimated gaze with the target plane was also analyzed throughout 38-min sessions. The results demonstrated high accuracy, with a minimum angular error of 2.5° using pupil positions, 2.06° using head orientation, and average errors of 4.46° and 3.19°, respectively. This approach was tested on a dataset of children with ASD to track their gaze towards a clinician, as this information is essential for assessing the children's social intent and interactions with others, facilitating more precise clinical assessments for children with autism.

Indexed as

autism spectrum disordercomputer visiongaze trackingRGB-D technology

Identifiers

PMID40281730
PMCPMC12025127

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.