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@ {Inproceedings

author = {Luis Unzueta and Jon Goenetxea },

title = {Cyclic and Non-Cyclic Gesture Spotting and Classification in Real-Time Applications },

year = {2010-07-09 },

keys = {Human-Computer Interaction Gesture Spotting Gesture Recognition Motion Pattern Motion Capture },

pages = {172- 181 },

abstract = {This paper presents a gesture recognition method for detecting and classifying both cyclic and non-cyclic human motion patterns in real-time applications. The semantic segmentation of a constantly captured human motion data stream is a key research topic, especially if both cyclic and non-cyclic gestures are considered during the human-computer interaction. The system measures the temporal coherence of the movements being captured according to its knowledge database, and once it has a sufficient level of certainty on its observation semantics the motion pattern is labeled automatically. In this way, our recognition method is also capable of handling time-varying dynamic gestures. The effectiveness of the proposed method is demonstrated via recognition experiments with a triple-axis accelerometer and a 3D tracker used by various performers. },

issn = {0302-9743 },

in = {Articulated Motion and Deformable Objects (AMDO 2010) },

}