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Roxana Peña-Mendieta1, Ania Mesa-Rodríguez1,2, Daniel Estevez-Moya2,3

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Summary
This summary is machine-generated.

This study classifies two-dimensional trajectories using entropic analysis of their coded representations. This approach, utilizing Kolmogorov-Sinai entropy, effectively categorizes complex motion patterns like the Hénon-Heiles model and human posture.

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Area of Science:

  • Dynamical Systems and Complexity Science
  • Information Theory
  • Computational Physics

Background:

  • Trajectory analysis is crucial in understanding complex systems.
  • Traditional methods may struggle with high-dimensional or noisy data.
  • Characterizing motion patterns requires robust quantitative measures.

Purpose of the Study:

  • To develop a novel method for classifying two-dimensional trajectories.
  • To apply entropic analysis to coded trajectory representations.
  • To demonstrate the method's versatility with diverse examples.

Main Methods:

  • Discretizing trajectories into an 8-symbol code using the Freeman procedure.
  • Applying entropic analysis, including Kolmogorov-Sinai entropy.
  • Utilizing effective complexity and informational distance measures.
  • Developing classification schemes based on entropy variables.

Main Results:

  • The entropic analysis of coded trajectories provides a robust classification framework.
  • The Hénon-Heiles model was successfully classified, validating the method's complexity analysis capabilities.
  • Human posture data was analyzed, showing the method's applicability to real-world experimental data.

Conclusions:

  • The proposed entropic analysis of coded trajectories offers a powerful tool for trajectory classification.
  • This method is adaptable to various complex systems, from theoretical models to experimental data.
  • The approach facilitates the differentiation and understanding of distinct motion dynamics.