Smartphone-based gaze estimation for in-home autism research.

Autor: Kim NY; Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, California, USA., He J; Google Research, Mountain View, California, USA., Wu Q; Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, California, USA., Dai N; Google Research, Mountain View, California, USA., Kohlhoff K; Google Research, Mountain View, California, USA., Turner J; Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, California, USA., Paul LK; Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, California, USA., Kennedy DP; Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, USA., Adolphs R; Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, California, USA.; Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA.; Chen Neuroscience Institute, California Institute of Technology, Pasadena, California, USA., Navalpakkam V; Google Research, Mountain View, California, USA.
Jazyk: angličtina
Zdroj: Autism research : official journal of the International Society for Autism Research [Autism Res] 2024 Jun; Vol. 17 (6), pp. 1140-1148. Date of Electronic Publication: 2024 Apr 25.
DOI: 10.1002/aur.3140
Abstrakt: Atypical gaze patterns are a promising biomarker of autism spectrum disorder. To measure gaze accurately, however, it typically requires highly controlled studies in the laboratory using specialized equipment that is often expensive, thereby limiting the scalability of these approaches. Here we test whether a recently developed smartphone-based gaze estimation method could overcome such limitations and take advantage of the ubiquity of smartphones. As a proof-of-principle, we measured gaze while a small sample of well-assessed autistic participants and controls watched videos on a smartphone, both in the laboratory (with lab personnel) and in remote home settings (alone). We demonstrate that gaze data can be efficiently collected, in-home and longitudinally by participants themselves, with sufficiently high accuracy (gaze estimation error below 1° visual angle on average) for quantitative, feature-based analysis. Using this approach, we show that autistic individuals have reduced gaze time on human faces and longer gaze time on non-social features in the background, thereby reproducing established findings in autism using just smartphones and no additional hardware. Our approach provides a foundation for scaling future research with larger and more representative participant groups at vastly reduced cost, also enabling better inclusion of underserved communities.
(© 2024 The Authors. Autism Research published by International Society for Autism Research and Wiley Periodicals LLC.)
Databáze: MEDLINE
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