Performance analysis of gesture recognition classifiers for building a human robot interface

In this paper we present a natural human computer interface based on gesture recognition. The principal aim is to study how different personalized gestures, defined by users, can be represented in terms of features and can be modelled by classification approaches in order to obtain the best performances in gesture recognition. Ten different gestures involving the movement of the left arm are performed by different users. Different classification methodologies (SVM, HMM, NN, and DTW) are compared and their performances and limitations are discussed. An ensemble of classifiers is proposed to produce more favorable results compared to those of a single classifier system. The problems concerning different lengths of gesture executions, variability in their representations, generalization ability of the classifiers have been analyzed and a valuable insight in possible recommendation is provided.