Trajectory classification through Freeman’s curve encoding and entropic analysis
Roxana Peña-Mendieta, Ania Mesa-Rodríguez, Daniel Estevez-Moya, José Rafael de la Horra, Ernesto Estevez-Rams, Holger Kantz
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.
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.
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Click any figure to enlarge with its caption.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
Figure 23
Figure 24
Figure 25
Figure 26
Figure 27
Figure 28
Figure 29
Figure 30
Figure 31
Figure 32
Figure 33
Figure 34
Figure 35
Figure 36
Figure 37
Figure 38
Figure 39
Figure 40
Figure 41
Figure 42
Figure 43
Figure 44
Figure 45
Figure 46
Figure 47
Figure 48
Figure 49
Figure 50Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMorphological variations and asymmetry · Motor Control and Adaptation · Fractal and DNA sequence analysis