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A comparison of automatic filtering techniques applied to biomechanical walking data

G Giakas1, V Baltzopoulos

  • 1Division of Sport, Health and Exercise, Staffordshire University, Stoke-on-Trent, UK.

Journal of Biomechanics
|August 1, 1997
PubMed
Summary

No single automatic filtering technique excels for all biomechanics gait analysis kinematic signals. Power spectrum estimation, least-squares cubic splines, and generalized cross-validation generally yielded the most acceptable results for kinematic signal processing.

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Area of Science:

  • Biomechanics
  • Biomedical Engineering
  • Signal Processing

Background:

  • Accurate kinematic data is crucial for gait analysis in biomechanics.
  • Automatic filtering techniques are widely used to process noisy kinematic signals.
  • Evaluating the performance of these filtering techniques is essential for reliable analysis.

Purpose of the Study:

  • To compare and evaluate six common automatic filtering techniques for biomechanics gait analysis kinematic signals.
  • To determine the optimal filtering method across various signal and noise conditions.

Main Methods:

  • Utilized 1440 synthetic gait kinematic signals with known noise characteristics.
  • Applied six filtering techniques: power spectrum (signal-to-noise ratio) assessment, generalized cross-validation spline, least-squares cubic splines, regularization of Fourier series, regression model, and residual analysis.

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  • Quantified performance using root mean square error (RMSE) between filtered and reference signals across derivative domains.

    • Main Results:

    • No single filtering technique demonstrated superior performance across all tested conditions.
    • Power spectrum estimation, least-squares cubic splines, and generalized cross-validation techniques generally provided the most acceptable results.
    • Performance varied depending on the specific signal and noise characteristics.

    Conclusions:

    • The choice of automatic filtering technique in biomechanics gait analysis should consider the specific signal and noise properties.
    • Power spectrum estimation, least-squares cubic splines, and generalized cross-validation are recommended as generally robust methods.
    • Further research may be needed to develop adaptive filtering strategies for optimal gait analysis.