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Updated: Jan 6, 2026

Postural Organization of Gait Initiation for Biomechanical Analysis Using Force Platform Recordings
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Methods for Gait Analysis During Obstacle Avoidance Task.

Dmitry Patashov1,2, Yakir Menahem3, Ohad Ben-Haim3

  • 1Faculty of Engineering, Holon Institute of Technology, Holon, Israel. DmitryP@hit.ac.il.

Annals of Biomedical Engineering
|October 11, 2019
PubMed
Summary

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This summary is machine-generated.

This study introduces robust algorithms for signal processing, enhancing gait analysis with novel methods for extremum estimation, signal segmentation, and missing data approximation. These tools improve the accuracy of analyzing noisy, quasi-periodic signals and kinematic data.

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Gait analysis and signal processing tasks often involve noisy, quasi-periodic data.
  • Accurate analysis requires robust methods for extremum estimation, segmentation, and handling missing data.
  • Existing methods may lack flexibility or robustness in real-world applications.

Purpose of the Study:

  • To develop a novel, general-purpose algorithm for extremum estimation in noisy, quasi-periodic signals.
  • To create a signal segmentation method for analyzing kinematic data during obstacle avoidance tasks.
  • To introduce algorithms for missing data approximation, adaptive filtering, and advanced machine learning-based classification.

Main Methods:

  • A flexible and robust algorithm for extremum estimation immune to noise and variability.
Keywords:
Adaptive filteringComplex envelopeDual multi-label forecastingKernel clusteringMissing dataNoisy signalPeak detectionQuasi-periodic signalSignal segmentation

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  • Signal segmentation to identify preparation and recovery phases in obstacle avoidance kinematic data.
  • Kernel-based clustering for unsupervised classification of walking steps and a predictive machine learning technique for supervised multiclass labeling.

    • Main Results:

    • Demonstrated a novel algorithm for accurate extremum estimation in challenging signal conditions.
    • Successfully segmented kinematic data to differentiate obstacle avoidance phases.
    • Developed an effective method for missing data approximation and adaptive signal filtering.
    • Achieved accurate classification of regular and abnormal steps and multiclass labeling.

    Conclusions:

    • The presented algorithms offer a flexible, robust, and accurate toolkit for signal processing, particularly in gait analysis.
    • These methods enhance the ability to analyze complex kinematic data, identify specific movement phases, and handle data imperfections.
    • The developed techniques have broad applicability beyond gait analysis, improving signal processing in various scientific and engineering domains.