Louis Kratz
Matthew Smith
Frank J. Lee
Summary:
Goal : Gesture recognition from 3D accelorometer data from Wiimote. Fluid tolerance and more immersive experience in gaming.
Paper evaluated a HMM model for recognizing Wii accelerometer gestures. The effect number of training data and number of states in HMM on the recognition speed and accuracy has been analyzed. The average correctness of HMM without training data is also measured which is around 50%. The HMM performed well at 15 states and after 10 samples per gesture yielded 80% accuracy and after 20 samples per gesture training 95% accuracy. The results showed that more states in HMM meant more training time and slower recognition speed.
Discussion:
I do not see what is novel about this system. Almost all the classifiers need some form of training so why not think about making the training process interesting or making the training part of the game.
The gestures they choose do not seem intuitive. More emphasis on the game rather than recognition would have been better.
ReplyDeleteI did find anything particularly interesting outside of the adaptive nature of the gesture difficulty.
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