Abstract—This paper proposes the use of interpolation
methods rather that conventional learning algorithms such as
Support Vector Machines (SVM) or Policy Learning by Weight
Exploration with Return (POWER) for modelling human
motion. The main aim was using a simpler model with less time
and space complexity for later use in the recognition of certain
actions. Three different polynomial interpolation methods,
namely Lagrange, spline and cubic spline have been
investigated. Parts of the dataset were used instead of the
complete dataset using grouping techniques to reduce the
training time. A non-parametric test known as Mc-Nemar's test
was used to identify statistically significant performance
differences between these methods. It was found that the cubic
spline resulted in better accuracy.
Index Terms—Interpolations, dynamic movement
primitives, learning algorithms, human motion.
Egemen Halici and Erkan Bostanci are with SAAT Lab., Computer
Engineering Department, Ankara University, Golbasi, Ankara, Turkey
(e-mail: egemenhalici@gmail.com, ebostanci@ankara.edu.tr).
[PDF]
Cite:Egemen Halici and Erkan Bostanci, "Evaluating the use of Interpolation Methods for Human Body Motion Modelling," International Journal of Computer Theory and Engineering vol. 10, no. 1, pp. 25-29, 2018.
