Human Body Motions Classification

Abstract

This paper deals with video based parameterization and classification of human body motions. The main task of this work is to develop and verify the procedures for observing of muscle and brain activity. The developed procedures have no negative impact to brain activity (the tracking does not affect the measured EEG signals). The procedures required only standard hardware equipment accessible on neurological laboratories. The body motions are non-contact sensed using a pair of standard DV camcorders. This work includes the description of observing, discerning and parameterization procedures and the discussion of motion classification. The set of classifiers — hierarchical clustering algorithm, recursive clustering algorithm, k-means classifier, Bayes classifier and classifier based on discrimination functions — was developed and implemented. The analysis of the classifiers properties was accomplished in this work. The accuracy of classification was tested for selected classifiers.