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Updated: Jun 26, 2026
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
Insights
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:
- 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.
Abstract:
Toe walking is commonly seen in children with neurological symptoms such as cerebral palsy. However idiopathic toe walking (ITW) in children is considered to be habitual. ITW children are categorized as toe walkers without any neurological problems, however they walk with their foot plantar-flexed. These children often suffer poor sport performance leading to low exercise levels and the associated consequences. If the condition is not treated, the ITW children eventually develop abnormal gait pattern as adults and could suffer from postural problems. However, ITW gait is difficult to observe since children can modify their gait when made aware of it. Gait analysis using heel accelerometry data in ITW children could provide an objective and quantitative description of their toe walking and may thus be beneficial for observing ITW. In this paper, we propose a technique based on Support Vector Machines (SVM) to recognize ITW gait patterns using heel accelerometry data. Test results indicated that the SVM is able to identify ITW gait patterns with a maximum accuracy of 87.5% when a feature selection algorithm was applied.
