Trajectory classification through Freeman’s curve encoding and entropic analysis

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.