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A Scalable Off-the-Shelf Framework for Measuring Patterns of Attention in Young Children and its Application in Autism Spectrum Disorder.

Publication ,  Journal Article
Bovery, M; Dawson, G; Hashemi, J; Sapiro, G
Published in: IEEE transactions on affective computing
July 2021

Autism spectrum disorder (ASD) is associated with deficits in the processing of social information and difficulties in social interaction, and individuals with ASD exhibit atypical attention and gaze. Traditionally, gaze studies have relied upon precise and constrained means of monitoring attention using expensive equipment in laboratories. In this work we develop a low-cost off-the-shelf alternative for measuring attention that can be used in natural settings. The head and iris positions of 104 16-31 months children, an age range appropriate for ASD screening and diagnosis, 22 of them diagnosed with ASD, were recorded using the front facing camera in an iPad while they watched on the device screen a movie displaying dynamic stimuli, social stimuli on the left and nonsocial stimuli on the right. The head and iris position were then automatically analyzed via computer vision algorithms to detect the direction of attention. Children in the ASD group paid less attention to the movie, showed less attention to the social as compared to the nonsocial stimuli, and often fixated their attention to one side of the screen. The proposed method provides a low-cost means of monitoring attention to properly designed stimuli, demonstrating that the integration of stimuli design and automatic response analysis results in the opportunity to use off-the-shelf cameras to assess behavioral biomarkers.

Duke Scholars

Published In

IEEE transactions on affective computing

DOI

EISSN

1949-3045

ISSN

1949-3045

Publication Date

July 2021

Volume

12

Issue

3

Start / End Page

722 / 731

Related Subject Headings

  • 4608 Human-centred computing
  • 4603 Computer vision and multimedia computation
  • 4602 Artificial intelligence
  • 1702 Cognitive Sciences
  • 0806 Information Systems
  • 0801 Artificial Intelligence and Image Processing
 

Citation

APA
Chicago
ICMJE
MLA
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Bovery, M., Dawson, G., Hashemi, J., & Sapiro, G. (2021). A Scalable Off-the-Shelf Framework for Measuring Patterns of Attention in Young Children and its Application in Autism Spectrum Disorder. IEEE Transactions on Affective Computing, 12(3), 722–731. https://doi.org/10.1109/taffc.2018.2890610
Bovery, Matthieu, Geraldine Dawson, Jordan Hashemi, and Guillermo Sapiro. “A Scalable Off-the-Shelf Framework for Measuring Patterns of Attention in Young Children and its Application in Autism Spectrum Disorder.IEEE Transactions on Affective Computing 12, no. 3 (July 2021): 722–31. https://doi.org/10.1109/taffc.2018.2890610.
Bovery M, Dawson G, Hashemi J, Sapiro G. A Scalable Off-the-Shelf Framework for Measuring Patterns of Attention in Young Children and its Application in Autism Spectrum Disorder. IEEE transactions on affective computing. 2021 Jul;12(3):722–31.
Bovery, Matthieu, et al. “A Scalable Off-the-Shelf Framework for Measuring Patterns of Attention in Young Children and its Application in Autism Spectrum Disorder.IEEE Transactions on Affective Computing, vol. 12, no. 3, July 2021, pp. 722–31. Epmc, doi:10.1109/taffc.2018.2890610.
Bovery M, Dawson G, Hashemi J, Sapiro G. A Scalable Off-the-Shelf Framework for Measuring Patterns of Attention in Young Children and its Application in Autism Spectrum Disorder. IEEE transactions on affective computing. 2021 Jul;12(3):722–731.

Published In

IEEE transactions on affective computing

DOI

EISSN

1949-3045

ISSN

1949-3045

Publication Date

July 2021

Volume

12

Issue

3

Start / End Page

722 / 731

Related Subject Headings

  • 4608 Human-centred computing
  • 4603 Computer vision and multimedia computation
  • 4602 Artificial intelligence
  • 1702 Cognitive Sciences
  • 0806 Information Systems
  • 0801 Artificial Intelligence and Image Processing