# Trajectory classification through Freeman’s curve encoding and entropic analysis

## TL;DR

This paper uses entropy analysis of coded trajectory data to classify movement patterns in two dimensions.

## Contribution

A novel method for trajectory classification using Freeman’s encoding and entropic measures is introduced.

## Key findings

- Freeman’s 8-symbol encoding effectively represents trajectories for entropic analysis.
- Entropy measures like Kolmogorov-Sinai and informational distance help classify trajectory complexity.
- The method is validated using the Hénon-Heiles model and human posture data.

## Abstract

The classification of trajectories in two dimensions was done through an entropic analysis of their coded representation. The steps include discretising the trajectory into an 8-symbol code using the Freeman procedure. The resulting sequence is amenable to entropic analysis. Kolmogorov-Sinai entropy, effective complexity measure and informational distance are used. Different classification schemes can be used based on the value of the entropy variables. Two examples are discussed to illustrate the approach: the Hénon-Heiles model, often used as a test bench for complexity analysis and a real experimental case of human posture analysis.