Velocity Profile Based Recognition of Dynamic Gestures with Discrete Hidden Markov Models
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Interaction design and experiential factors: a novel case study on digital pen and paper
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Detecting gesture force peaks for intuitive interaction
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Gesture Recognition with a 3-D Accelerometer
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UbiGesture: Customizing and Profiling Hand Gestures in Ubiquitous Environment
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Proceedings of the 21st International Conference on Association Francophone d'Interaction Homme-Machine
Fusion engines for multimodal input: a survey
Proceedings of the 2009 international conference on Multimodal interfaces
SOMM: Self organizing Markov map for gesture recognition
Pattern Recognition Letters
Multimodal interaction with speech and physical touch interface in a media center application
Proceedings of the International Conference on Advances in Computer Enterntainment Technology
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The $3 recognizer: simple 3D gesture recognition on mobile devices
Proceedings of the 15th international conference on Intelligent user interfaces
3D site surveyor for 360° wireless motion tracking
International Journal of Virtual Technology and Multimedia
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Computers in Biology and Medicine
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An introduction to 3D spatial interaction with video game motion controllers
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Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia
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Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia
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In many applications today user interaction is moving away from mouse and pens and is becoming pervasive and much more physical and tangible. New emerging interaction technologies allow developing and experimenting with new interaction methods on the long way to providing intuitive human computer interaction. In this paper, we aim at recognizing gestures to interact with an application and present the design and evaluation of our sensor-based gesture recognition. As input device we employ the Wii-controller (Wiimote) which recently gained much attention world wide. We use the Wiimote's acceleration sensor independent of the gaming console for gesture recognition. The system allows the training of arbitrary gestures by users which can then be recalled for interacting with systems like photo browsing on a home TV. The developed library exploits Wii-sensor data and employs a hidden Markov model for training and recognizing user-chosen gestures. Our evaluation shows that we can already recognize gestures with a small number of training samples. In addition to the gesture recognition we also present our experiences with the Wii-controller and the implementation of the gesture recognition. The system forms the basis for our ongoing work on multimodal intuitive media browsing and are available to other researchers in the field.