Exploring strategies and guidelines for developing full body video game interfaces
Proceedings of the Fifth International Conference on the Foundations of Digital Games
An introduction to 3D spatial interaction with video game motion controllers
ACM SIGGRAPH 2010 Courses
Protractor3D: a closed-form solution to rotation-invariant 3D gestures
Proceedings of the 16th international conference on Intelligent user interfaces
3D spatial interaction: applications for art, design, and science
ACM SIGGRAPH 2011 Courses
Wearable mobile augmented reality: evaluating outdoor user experience
Proceedings of the 10th International Conference on Virtual Reality Continuum and Its Applications in Industry
6DMG: a new 6D motion gesture database
Proceedings of the 3rd Multimedia Systems Conference
Wizard of Wii: toward understanding player experience in first person games with 3D gestures
Proceedings of the 6th International Conference on Foundations of Digital Games
An exploratory study of user-generated spatial gestures with social mobile devices
Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia
The impact of motion dimensionality and bit cardinality on the design of 3D gesture recognizers
International Journal of Human-Computer Studies
Proceedings of the 2013 international conference on Intelligent user interfaces
There's a world outside your TV: exploring interactions beyond the physical TV screen
Proceedings of the 11th european conference on Interactive TV and video
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We present a systematic study on the recognition of 3D gestures using spatially convenient input devices. Specifically, we examine the linear acceleration-sensing Nintendo Wii Remote coupled with the angular velocity-sensing Nintendo Wii MotionPlus. For the study, we created a 3D gesture database, collecting data on 25 distinct gestures totalling 8500 gestures samples. Our experiment explores how the number of gestures and the amount of gestures samples used to train two commonly used machine learning algorithms, a linear and AdaBoost classifier, affect overall recognition accuracy. We examined these gesture recognition algorithms with user dependent and user independent training approaches and explored the affect of using the Wii Remote with and without the Wii MotionPlus attachment. Our results show that in the user dependent case, both the Ad-aBoost and linear classification algorithms can recognize up to 25 gestures at over 90% accuracy, with 15 training samples per gesture, and up to 20 gestures at over 90% accuracy, with only five training samples per gesture. In particular, all 25 gestures could be recognized at over 99% accuracy with the linear classifier using 15 training samples per gesture, with the Wii Remote coupled with the Wii MotionPlus. In addition, both algorithms can recognize up to nine gestures at over 90% accuracy using a user independent training database with 100 samples per gesture. The Wii MotionPlus attachment played a significant role in improving accuracy in both the user dependent and independent cases.