Selection of Relevant Parameters for Human Locomotion Unsupervised Classification

Abstract

A method for the automatic selection of the most relevant parameters for human locomotion classification is proposed. A set of 36 statistical parameters extracted from video sequences showing three basic movement types is used. Because the unsupervised classification is based on the k-means clustering algorithm, the sets of relevant parameters are determined by applying binary optimization metaheuristics using a clustering evaluation measure as objective function. Considering that the objective function is multimodal, all combinations which maximize it are retained. The binary versions of Particle Swarm and Black Hole algorithms were modified to manage the multiple solutions of the optimization process. The experiments revealed that the Black Hole algorithm leads to better results, even if it is considered a simplified version of the Particle Swarm Algorithm.

Keywords

• human locomotion
• k-means clustering
• binary optimization
• nature-inspired Metaheuristics