Evaluation of Genetic Programs in Multiple Cases for Gait Classification and Recognition

Abstract

The study of human gait is one of the interesting fields among several disciplines. This arena has attracted researchers and professionals not only from human physiology and neuro biology but has also fascinated scientists from socio-psychology and socio-informatics. The locomotion of humans is associated with lots of physical and biological aspect that might provide critical information related to stress, intentions, emotions, personality, and even neurological disorder. This abundant source of data might be used in the complex system to create information in response to generate evidences for identifying the particular person, some of which was also investigated in our previous work (1). This paper is the extension of human gait recognition that presents the analysis of trade-off between evolution of Genetic Programs (GPs) and their performance, considering different training cases, provided that the computational effort and some other parameter are kept constant. Furthermore, our previous work (2) had left an important question unanswered about how the extension of number of fitness cases and the use of experts in collaborative filtering affects the evolution of GPs and recognition of Gait. This study is an attempt to explore the same unexplained question.