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Enhancement of force patterns classification based on Gaussian distributions.

Thomas Ertelt1, Ilja Solomonovs2, Thomas Gronwald2

  • 1Motions Science/Biomechanics, Faculty of Sport Science, University of Applied Sciences in Health and Sports, Berlin, Germany.

Journal of Biomechanics
|December 26, 2017
PubMed
Summary

A new method combines Gaussian distributions and Bayesian neural networks to objectively classify ground reaction force patterns. This approach accurately distinguishes between different sports based on force data, improving diagnostic capabilities in medicine and biomechanics.

Keywords:
DiagnosticsForce functionGait analysisNeural network

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Area of Science:

  • Biomechanics
  • Machine Learning
  • Medical Diagnostics

Background:

  • Ground reaction force (GRF) pattern analysis is crucial in medicine, biomechanics, and robotics.
  • Current visual classification of GRF time-courses relies heavily on expert experience, lacking objectivity.
  • A standardized, objective method is needed for discriminating GRF patterns, particularly in clinical settings.

Purpose of the Study:

  • To develop a novel, overarching method for objective GRF pattern classification.
  • To combine machine learning and Gaussian approximation for enhanced GRF analysis.
  • To overcome limitations of existing individual methods for GRF discrimination.

Main Methods:

  • Twenty-nine male athletes performed a one-legged stopping maneuver on a force plate.
  • Individual GRF time-courses were recorded and approximated using eight Gaussian distributions.
  • Descriptive coefficients were classified using Bayesian regulated neural networks, with sport as the distinguishing feature.

Main Results:

  • Distinct GRF time-course qualities were identified across different sports, despite a uniform task.
  • High homogeneity was observed within athletes of the same sport.
  • The combined method achieved 94.29% accuracy in classifying subjects/sports (R=0.938).

Conclusions:

  • The integration of Gaussian distribution-based GRF description and Bayesian neural network classification is effective.
  • This method provides an adequate and promising approach for information-preserving GRF discrimination.
  • The findings suggest potential for improved objective diagnostics in sports and medicine.