Dynamic movement primitives for human movement recognition

Abstract

Dynamic Movement Primitives (DMPs)-originally a method for movement trajectory generation [1] has been also used for recognition tasks [2, 3]. However there has not been a systematic comparison between other recognition methods and DMPs using human movement data. This paper presents a comparison of commonly used Hidden Markov Model (HMM) based recognition with DMP based recognition using human generated letter trajectories. As the working principles of these two methods are very different, in addition to the performance, the numbers of adaptable parameters that are used in each method and, process time were compared. The results, indicate that HMM gives better results than DMP, with possible noise robustness advantage in DMPs for human movement.