Breaking the status quo: Improving 3D gesture recognition with spatially convenient input devices

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 AdaBoost and linear classification algorithms can recognize up to 25 gestures at over 90% accuracy, with 15 training samples per gesture, and...

• Type: Journal
• Year: 2010
• Where: VR
• Authors: Michael Hoffman, Paul Varcholik, Joseph J. LaViola Jr.
• Keywords: Gesture, Virtual Reality, VR 2010, Wii MotionPlus, Wii Remote