Assessing Children's Fine Motor Skills With Sensor-Augmented Toys: Machine Learning Approach.

Journal: Journal of medical Internet research
Published Date: Apr 22, 2021

Abstract

BACKGROUND: Approximately 5%-10% of elementary school children show delayed development of fine motor skills. To address these problems, detection is required. Current assessment tools are time-consuming, require a trained supervisor, and are not motivating for children. Sensor-augmented toys and machine learning have been presented as possible solutions to address this problem.

Authors

  • Annette Brons
    Digital Life Center, Amsterdam University of Applied Sciences, Amsterdam, Netherlands.
  • Antoine de Schipper
    Academy for Physical Education, Amsterdam University of Applied Sciences, Amsterdam, Netherlands.
  • Svetlana Mironcika
    Play and Civic Media, Amsterdam University of Applied Sciences, Amsterdam, Netherlands.
  • Huub Toussaint
    Academy for Physical Education, Amsterdam University of Applied Sciences, Amsterdam, Netherlands.
  • Ben Schouten
    Play and Civic Media, Amsterdam University of Applied Sciences, Amsterdam, Netherlands.
  • Sander Bakkes
    Department of Information and Computing Sciences, Utrecht University, Utrecht, Netherlands.
  • Ben Kröse
    Faculty of Digital Media and Creative Industries, Digital Life Centre, University of Applied Sciences Amsterdam, Amsterdam, Netherlands.

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