Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A state-based technique for the summarization and recognition of gesture
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Computational Analysis of Mannerism Gestures
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
3D Motion Recognition based on Ensemble Learning
WIAMIS '07 Proceedings of the Eight International Workshop on Image Analysis for Multimedia Interactive Services
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In this paper, we consider how low cost wearable sensors may be used to enhance mobile interactions with learning and games multimedia. Unlike camera and single accelerometer based systems, the sensors developed provide measurements for all of the user's limb movements and this data can then be used to recognize what the user is doing anytime anywhere. The movements may then drive a game or may be used in an educative or artistic experience. The aim here is to report on work done using the Hidden Markov Model method for gesture recognition applied as a component within an exemplary Tai Chi training system. The intention is to demonstrate a practical result which could form the basis for other researchers involved in future mobile applications such as dance training, martial arts and sports. We also look at an extension of this work in the field of interactive dance multimedia.