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Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
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Segmentation and classification of gait cycles.

Valentina Agostini, Gabriella Balestra, Marco Knaflitz

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |April 25, 2014
    PubMed
    Summary

    This study presents an automated algorithm for analyzing gait cycles using foot-switch data. The method accurately segments and classifies gait, proving effective for both healthy and pathological gaits without needing specific templates.

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    Area of Science:

    • Biomechanics
    • Rehabilitation Engineering
    • Clinical Gait Analysis

    Background:

    • Instrumented gait analysis, particularly using foot-switches, is crucial for studying gait abnormalities and foot-floor contact.
    • Analyzing long walks reduces variability but necessitates automated, user-independent methods for processing numerous gait cycles.
    • Existing methods may require pathology-specific templates, limiting their broad applicability.

    Purpose of the Study:

    • To develop and validate an algorithm for automatic segmentation and classification of gait cycles from foot-switch signals.
    • To assess the algorithm's performance against expert manual analysis.
    • To enable the identification of atypical gait cycles across various pathological conditions.

    Main Methods:

    • Development of an algorithm for segmenting foot-switch signals into distinct gait phases.

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  • Classification of individual gait cycles based on the segmented data.
  • Validation by comparing algorithm results with manual segmentation and classification by a gait analysis expert.

    • Main Results:

    • The algorithm achieved 100% performance for healthy subjects.
    • Performance exceeded 98% for pathological subjects.
    • The algorithm successfully identified atypical gait cycles without relying on pathology-specific templates.

    Conclusions:

    • The developed algorithm provides a reliable and accurate method for gait cycle segmentation and classification.
    • It is effective for analyzing both healthy and diverse pathological gaits, offering a user-independent solution.
    • This tool can aid in the objective assessment of gait disorders and the identification of deviations from normal gait patterns.