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Automatic algorithm for filtering kinematic signals with impacts in the Wigner representation.

A Georgakis1, L K Stergioulas, G Giakas

  • 1Department of Communication Systems, Lancaster University, Lancaster, UK. a.georgakis@lancaster.ac.uk

Medical & Biological Engineering & Computing
|January 1, 2003
PubMed
Summary

A new automatic filtering algorithm accurately estimates kinematic signal derivatives during impacts. This method outperforms existing techniques, offering significant improvements for analyzing high-impact events.

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Area of Science:

  • Biomechanics
  • Signal Processing
  • Mechanical Engineering

Background:

  • Accurate estimation of second derivatives of kinematic signals is crucial for analyzing impact events.
  • Traditional methods struggle with the non-stationarities introduced by impacts.

Purpose of the Study:

  • To propose an automatic filtering algorithm for accurate estimation of second derivatives of kinematic signals during impacts.
  • To address the challenges posed by signal non-stationarities caused by impacts.

Main Methods:

  • The algorithm employs time-frequency filtering in the Wigner representation.
  • Filtering parameters are adaptively adjusted to individual signal characteristics.
  • Performance was evaluated against linear phase autoregressive model-based derivative assessment (LAMBDA) and generalised cross-validation using quintic splines (GCVQS).

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Main Results:

  • The proposed method achieved an average absolute relative error of 5.7% for peak acceleration in high-impact scenarios.
  • This significantly outperforms Butterworth low-pass filter (17.2%), LAMBDA (18.3%), and GCVQS (37.2%).
  • For low-impact signals, time-frequency filtering was found to be unnecessary, with errors around 19.4%.

Conclusions:

  • The proposed automatic filtering algorithm provides a more accurate method for estimating second derivatives of kinematic signals during impacts.
  • It offers significant advantages over conventional methods, particularly for high-impact events.
  • The algorithm's adaptive nature enhances its applicability to diverse kinematic signals.