Feature selection of motion capture data in gait identification challenge problem

Abstract

The method of discovering robust gait signatures containing strong discriminative properties is proposed. It is based on feature extraction and selection of motion capture data. Three different approaches of feature extraction applied to Euler angles and their first and second derivates are considered. The proper supervised classification is preceded by specified selection scenario. On the basis of the obtained precision of person gait identification, analyzed feature sets are assessed. To examine proposed method database containing 353 gaits of 25 different males is used. The results are satisfactory. In the best case the recognition accuracy of 97% is achieved. On the basis of classification which takes into consideration only the data of the specified segments, the ranking is constructed. It corresponds to the evaluation of individual features of the joint movements. © 2014 Springer International Publishing Switzerland.