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Postural Organization of Gait Initiation for Biomechanical Analysis Using Force Platform Recordings
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Movement Onset Detection Methods: A Comparison Using Force Plate Recordings.

Brendan L Pinto1, Jack P Callaghan1

  • 1Department of Kinesiology & Health Sciences, University of Waterloo, Waterloo, ON,Canada.

Journal of Applied Biomechanics
|March 13, 2023
PubMed
Summary
This summary is machine-generated.

This study compared movement onset detection methods for biomechanical analysis. The first derivative method with a 10-Hz low-pass filter demonstrated the best agreement with manual onset selection.

Keywords:
errorfilterphasestarttime series

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Area of Science:

  • Biomechanics
  • Movement Analysis
  • Human Performance

Background:

  • Computational methods offer standardized and automated analysis of biomechanical signals.
  • Accurate movement onset detection is crucial for analyzing time-varying data like force-time recordings.
  • Existing methods, including the 5x standard deviation (SD) threshold, require further evaluation alongside less-studied techniques.

Purpose of the Study:

  • To compare the accuracy of the 5x SD threshold method, reverse scanning variations, and first derivative variations against manual onset selection.
  • To evaluate these methods in the context of the countermovement jump and squat exercises.
  • To determine the optimal filtering approach for movement onset detection.

Main Methods:

  • Manual selection of movement onsets from unfiltered force-time data.
  • Application of the 5x SD threshold method.
  • Implementation and evaluation of three variations of the reverse scanning method.
  • Implementation and evaluation of five variations of the first derivative method, with and without low-pass filtering at 10 Hz.

Main Results:

  • The first derivative method, when applied after a 10-Hz low-pass filter, exhibited the best limits of agreement with manually selected onsets (-0.02 to 0.05 s for countermovement jump, -0.07 to 0.11 s for squat).
  • Filtering the data before calculating the first derivative is essential to minimize the amplification of high frequencies.
  • The first derivative approach proved less sensitive to variations in the pre-movement quiet phase compared to other tested methods.

Conclusions:

  • The first derivative method, combined with a 10-Hz low-pass filter, is recommended for accurate and reliable movement onset detection in biomechanical analyses.
  • Filtering is a necessary preprocessing step for the first derivative method, even when analyzing unfiltered data.
  • This approach enhances the robustness and reduces variability in movement onset detection compared to other computational methods.