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Alvari G, Coviello L, Furlanello C. EYE-C: Eye-Contact Robust Detection and Analysis during Unconstrained Child-Therapist Interactions in the Clinical Setting of Autism Spectrum Disorders. Brain Sci. 2021;11(12)doi: 10.3390/brainsci11121555.
Alvari, G., Coviello, L., & Furlanello, C. (2021). EYE-C: Eye-Contact Robust Detection and Analysis during Unconstrained Child-Therapist Interactions in the Clinical Setting of Autism Spectrum Disorders. Brain sciences, 11(12), . https://doi.org/10.3390/brainsci11121555
Alvari, Gianpaolo, et al. "EYE-C: Eye-Contact Robust Detection and Analysis during Unconstrained Child-Therapist Interactions in the Clinical Setting of Autism Spectrum Disorders." Brain sciences vol. 11,12 (2021). doi: https://doi.org/10.3390/brainsci11121555
Alvari G, Coviello L, Furlanello C. EYE-C: Eye-Contact Robust Detection and Analysis during Unconstrained Child-Therapist Interactions in the Clinical Setting of Autism Spectrum Disorders. Brain Sci. 2021 Nov 24;11(12). doi: 10.3390/brainsci11121555. PMID: 34942856.
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Brain Sci. 2021 Nov 24;11(12). doi: 10.3390/brainsci11121555.

EYE-C: Eye-Contact Robust Detection and Analysis during Unconstrained Child-Therapist Interactions in the Clinical Setting of Autism Spectrum Disorders.

Brain sciences

Gianpaolo Alvari, Luca Coviello, Cesare Furlanello

Affiliations

  1. Department of Psychology and Cognitive Sciences, University of Trento, Corso Bettini 84, 38068 Rovereto, Italy.
  2. DSH Research Unit, Bruno Kessler Foundation, Via Sommarive 8, 38123 Trento, Italy.
  3. University of Trento, 38122 Trento, Italy.
  4. Enogis, Via al Maso Visintainer 8, 38122 Trento, Italy.
  5. HK3 Lab, Piazza Manifatture 1, 38068 Rovereto, Italy.
  6. Orobix Life, Via Camozzi 145, 24121 Bergamo, Italy.

PMID: 34942856 DOI: 10.3390/brainsci11121555

Abstract

The high level of heterogeneity in Autism Spectrum Disorder (ASD) and the lack of systematic measurements complicate predicting outcomes of early intervention and the identification of better-tailored treatment programs. Computational phenotyping may assist therapists in monitoring child behavior through quantitative measures and personalizing the intervention based on individual characteristics; still, real-world behavioral analysis is an ongoing challenge. For this purpose, we designed EYE-C, a system based on OpenPose and Gaze360 for fine-grained analysis of eye-contact episodes in unconstrained therapist-child interactions via a single video camera. The model was validated on video data varying in resolution and setting, achieving promising performance. We further tested EYE-C on a clinical sample of 62 preschoolers with ASD for spectrum stratification based on eye-contact features and age. By unsupervised clustering, three distinct sub-groups were identified, differentiated by eye-contact dynamics and a specific clinical phenotype. Overall, this study highlights the potential of Artificial Intelligence in categorizing atypical behavior and providing translational solutions that might assist clinical practice.

Keywords: autism spectrum disorders; behavior imaging; computational phenotyping; eye contact; heterogeneity; preschool children

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