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Related Experiment Video

Updated: Jun 26, 2026

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
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Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Published on: March 4, 2018

Detecting idiopathic toe-walking gait pattern from normal gait pattern using heel accelerometry data and Support

Gita Pendharkar1, Daniel T H Lai, Rezaul K Begg

  • 1Department of Electrical and Computer Systems Engineering, Monash University, Melbourne 3168, Australia. gita@eng.monash.edu.au

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 24, 2009
PubMed
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Idiopathic toe walking (ITW) in children, a habitual condition without neurological issues, can lead to long-term gait and postural problems. This study developed a Support Vector Machine (SVM) technique using heel accelerometry to objectively identify ITW gait patterns.

Area of Science:

  • Biomechanical analysis
  • Pediatric gait disorders
  • Machine learning applications in healthcare

Background:

  • Idiopathic toe walking (ITW) is a common condition in children, characterized by habitual plantar-flexed foot placement without underlying neurological deficits.
  • Untreated ITW can result in abnormal adult gait patterns, poor athletic performance, and potential postural issues.
  • Observing ITW gait is challenging as children may alter their walking when aware of being monitored.

Purpose of the Study:

  • To propose and evaluate a novel technique for the objective recognition of idiopathic toe walking (ITW) gait patterns.
  • To utilize heel accelerometry data for quantitative gait analysis in children with ITW.
  • To assess the effectiveness of Support Vector Machines (SVM) in identifying ITW gait.

Main Methods:

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  • Collection of heel accelerometry data from children exhibiting ITW.
  • Development of a gait pattern recognition technique employing Support Vector Machines (SVM).
  • Application of a feature selection algorithm to optimize SVM performance.

Main Results:

  • The proposed SVM-based technique demonstrated the ability to recognize ITW gait patterns.
  • A maximum accuracy of 87.5% was achieved in identifying ITW gait when a feature selection algorithm was incorporated.
  • Heel accelerometry data provided an objective measure for ITW gait analysis.

Conclusions:

  • Support Vector Machines (SVM) offer a promising approach for the objective and quantitative analysis of idiopathic toe walking (ITW).
  • The developed technique using heel accelerometry and SVM can aid in the accurate identification of ITW gait patterns.
  • Objective gait analysis is crucial for understanding and potentially treating ITW, mitigating long-term consequences.