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Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
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Combined Regularized Discriminant Analysis and Swarm Intelligence Techniques for Gait Recognition.

Tomasz Krzeszowski1, Krzysztof Wiktorowicz1

  • 1Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, al. Powstancow Warszawy 12, 35-959 Rzeszow, Poland.

Sensors (Basel, Switzerland)
|December 2, 2020
PubMed
Summary

This study introduces hybrid methods combining regularized discriminant analysis (RDA) with swarm intelligence for enhanced gait recognition. These novel approaches improve classification accuracy compared to traditional methods.

Keywords:
biometricsgait recognitiongrey wolf optimizationparticle swarm optimizationregularized discriminant analysiswhale optimization algorithm

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Area of Science:

  • Computer Vision
  • Biometrics
  • Machine Learning

Background:

  • Gait recognition research often focuses on feature extraction rather than classification.
  • Existing classification methods may not fully leverage the potential of gait data.

Purpose of the Study:

  • To develop advanced hybrid classification strategies for gait recognition.
  • To outperform conventional classification techniques in gait analysis.

Main Methods:

  • Hybridization of Regularized Discriminant Analysis (RDA) with swarm intelligence algorithms.
  • Utilizing Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA).
  • Optimizing RDA observation weights and hyperparameters via swarm intelligence for objective function minimization.

Main Results:

  • Experimental validation on the GPJATK dataset demonstrated the effectiveness of the proposed hybrid methods.
  • The optimized RDA approach showed superior performance in gait recognition tasks.

Conclusions:

  • The proposed hybrid classification framework offers a promising advancement in gait recognition.
  • Swarm intelligence effectively enhances RDA for improved biometric identification through gait analysis.