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Updated: Aug 23, 2025

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Classification of Autism and Control Gait in Children Using Multisegment Foot Kinematic Features.

Ashirbad Pradhan1, Victoria Chester2, Karansinh Padhiar2

  • 1Engineering Bionics Lab, Department of Systems Design Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON N2L6G2, Canada.

Bioengineering (Basel, Switzerland)
|October 27, 2022
PubMed
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Multisegment foot (MSF) kinematics combined with machine learning (ML) accurately classify autism gait patterns in children. This approach, focusing on foot mechanics, achieved 96.3% accuracy, outperforming single-segment foot (SSF) methods.

Area of Science:

  • Biomechanical analysis
  • Autism spectrum disorder research
  • Machine learning applications

Background:

  • Children with autism exhibit atypical walking patterns, specifically in ankle kinematics and kinetics.
  • Previous studies relied on single-segment foot (SSF) data, limiting detailed analysis of foot mechanics.

Purpose of the Study:

  • To investigate the utility of multisegment foot (MSF) kinematics and machine learning (ML) for classifying autism gait patterns.
  • To identify the most influential kinematic features in distinguishing autism gait from typical gait.

Main Methods:

  • Utilized a 34-marker system and 12-camera motion capture to record walking trials in 19 children with autism and 21 controls.
  • Extracted SSF and MSF kinematic features, developing support vector machine (SVM) models.
Keywords:
autismgaitkinematicsmachine learningmultisegment foot

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  • Employed Shapley Additive exPlanations (SHAP) and Shapley Additive Global importance (SAGE) for model interpretability.

    • Main Results:

    • MSF-based ML models achieved 96.3% accuracy in classifying autism gait patterns, significantly higher than SSF models (83.8%).
    • The relative angle between metatarsal and midfoot segments was the most critical feature for classification.
    • SHAP and SAGE analyses provided insights into the importance of specific kinematic features.

    Conclusions:

    • MSF kinematic features, analyzed with ML models, offer a highly accurate and interpretable method for classifying autism gait in children.
    • This advanced biomechanical approach enhances our understanding of gait differences in autism.