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Updated: Jan 10, 2026

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
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Non-Linear Modeling of Motor Development in Typically Developing Children and Youth Aged 5-18 Years Using Robot-Based

Stephan C D Dobri1, Stephen H Scott2, T Claire Davies1

  • 1Department of Mechanical and Materials Engineering, Queen's University, Kingston, ON K7L 3N6, Canada.

Bioengineering (Basel, Switzerland)
|November 27, 2025
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Summary

Robotic assessments create accurate models of motor development in children and youth. These models reveal that performance improves and variability decreases with age, crucial for identifying motor deficits.

Keywords:
adolescentchildrenmotor developmentmotor functionnon-linearreaction timerobotic assessmentsimulationsyouth

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Area of Science:

  • Neuroscience
  • Developmental Biology
  • Robotics

Background:

  • Clinical assessments for motor function lack accuracy and repeatability compared to robotic tasks.
  • Motor development follows a non-linear trajectory, with rapid changes in early life and slowing in adulthood.
  • Performance variability in motor tasks changes significantly across childhood and adolescence.

Purpose of the Study:

  • To develop normative models of motor development in typically developing children and youth using robotic assessments.
  • To establish age-specific performance benchmarks for tracking motor skill acquisition.
  • To investigate the relationship between age, performance variability, and repeatability.

Main Methods:

  • Utilized the Kinarm Exoskeleton robotic system for precise motor assessments.
  • Recruited 288 typically developing participants aged 5-18 years.
  • Applied exponential or quadratic curve fitting to robotic performance data, incorporating a linear term for age-related variability.

Main Results:

  • Most motor performance parameters demonstrated significant improvement with increasing age.
  • No parameters indicated a decline in performance with age.
  • Observed substantial age-related changes in performance variability, with some parameters showing up to a 74% reduction in range.
  • Reduced performance variability correlates with increased age.

Conclusions:

  • Normative models of motor development must account for age-related changes in performance variability and repeatability.
  • Accurate identification of motor deficits requires models that reflect these developmental changes.
  • Robotic assessments provide a reliable foundation for creating sophisticated models of typical motor development.