Kinetic pseudo-energy history for human dynamic gestures recognition

Abstract

In this paper we present a new approach, based on the kinetic status history, to automatically determine the starting and ending instants of human dynamic gestures. This method opens up the possibility to distinguish static or quasi-static poses from dynamic actions, during a real-time human motion capture. This way a more complex Human-Computer Interaction (HCI) can be attained. Along with this procedure, we also present a novel method to recognize dynamic gestures independently from the velocity with which they have been performed. The efficiency of this approach is tested with gestures captured with a triple axis accelerometer, and recognized with different statistical classifiers, obtaining satisfactory results for real-time applications. © 2008 Springer-Verlag.