Manara - Qatar Research Repository
Browse
10.1007_s11257-023-09371-0.pdf (1.98 MB)

One size does not fit all: detecting attention in children with autism using machine learning

Download (1.98 MB)
journal contribution
submitted on 2024-01-03, 07:38 and posted on 2024-01-03, 07:44 authored by Bilikis Banire, Dena Al Thani, Marwa Qaraqe

Detecting the attention of children with autism spectrum disorder (ASD) is of paramount importance for desired learning outcome. Teachers often use subjective methods to assess the attention of children with ASD, and this approach is tedious and inefficient due to disparate attentional behavior in ASD. This study explores the attentional behavior of children with ASD and the control group: typically developing (TD) children, by leveraging machine learning and unobtrusive technologies such as webcams and eye-tracking devices to detect attention objectively. Person-specific and generalized machine models for face-based, gaze-based, and hybrid-based (face and gaze) are proposed in this paper. The performances of these three models were compared, and the gaze-based model outperformed the others. Also, the person-specific model achieves higher predictive power than the generalized model for the ASD group. These findings stress the direction of model design from traditional one-size-fits-all models to personalized models.

Other Information

Published in: User Modeling and User-Adapted Interaction
License: https://creativecommons.org/licenses/by/4.0
See article on publisher's website: https://dx.doi.org/10.1007/s11257-023-09371-0

Funding

Open Access funding provided by the Qatar National Library.

History

Language

  • English

Publisher

Springer Nature

Publication Year

  • 2023

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Hamad Bin Khalifa University
  • College of Science and Engineering - HBKU

Usage metrics

    Qatar University

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC