ML Anthology

Detecting Autism by Analyzing a Simulated Social Interaction

Hanna Drimalla, Niels Landwehr, Irina Baskow, Behnoush Behnia, Stefan Roepke, Isabel Dziobek, Tobias Scheffer
ECML-PKDD 2018 pp. 193-208
doi:10.1007/978-3-030-10925-7_12 /ecmlpkdd/2018/drimalla2018ecmlpkdd-detecting/

Abstract

Diagnosing autism spectrum conditions takes several hours by well-trained practitioners; therefore, standardized questionnaires are widely used for first-level screening. Questionnaires as a diagnostic tool, however, rely on self-reflection—which is typically impaired in individuals with autism spectrum condition. We develop an alternative screening mechanism in which subjects engage in a simulated social interaction. During this interaction, the subjects’ voice, eye gaze, and facial expression are tracked, and features are extracted that serve as input to a predictive model. We find that a random-forest classifier on these features can detect autism spectrum condition accurately and functionally independently of diagnostic questionnaires. We also find that a regression model estimates the severity of the condition more accurately than the reference screening method.

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Cite

Text

Drimalla et al. "Detecting Autism by Analyzing a Simulated Social Interaction." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2018. doi:10.1007/978-3-030-10925-7_12

Markdown

[Drimalla et al. "Detecting Autism by Analyzing a Simulated Social Interaction." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2018.](https://mlanthology.org/ecmlpkdd/2018/drimalla2018ecmlpkdd-detecting/) doi:10.1007/978-3-030-10925-7_12

BibTeX

@inproceedings{drimalla2018ecmlpkdd-detecting,
  title     = {{Detecting Autism by Analyzing a Simulated Social Interaction}},
  author    = {Drimalla, Hanna and Landwehr, Niels and Baskow, Irina and Behnia, Behnoush and Roepke, Stefan and Dziobek, Isabel and Scheffer, Tobias},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2018},
  pages     = {193-208},
  doi       = {10.1007/978-3-030-10925-7_12},
  url       = {https://mlanthology.org/ecmlpkdd/2018/drimalla2018ecmlpkdd-detecting/}
}