SCIENCE@home

Predicting ADHD Risk from Touch Interaction Data

Philipp Mock,Maike Tibus,A. Ehlis,H. Baayen,Peter Gerjets

Published 2018 in International Conference on Multimodal Interaction

ABSTRACT

This paper presents a novel approach for automatic prediction of risk of ADHD in schoolchildren based on touch interaction data. We performed a study with 129 fourth-grade students solving math problems on a multiple-choice interface to obtain a large dataset of touch trajectories. Using Support Vector Machines, we analyzed the predictive power of such data for ADHD scales. For regression of overall ADHD scores, we achieve a mean squared error of 0.0962 on a four-point scale (R² = 0.5667). Classification accuracy for increased ADHD risk (upper vs. lower third of collected scores) is 91.1%.

PUBLICATION RECORD

  • Publication year

    2018

  • Venue

    International Conference on Multimodal Interaction

  • Publication date

    2018-10-02

  • Fields of study

    Computer Science, Psychology

  • Identifiers
    DOI 10.1145/3242969.3242986
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-26 of 26 references · Page 1 of 1

CITED BY

Showing 1-10 of 10 citing papers · Page 1 of 1