Summary:
Kratz, Smith, and Lee use Wiimotes in a game where two wizards cast spells to damage one another. Each spell consists of a series of gestures and modifiers, and a wizard can block a spell by performing a blocking gesture and then mimicking their opponent's casting gestures.
Wii controller accelerometer data is used to gather a 3-dimensional gravitational reading for the three x, y, z axes. An observation vector is a collection of these data values, and Gaussians are applied to the observations to determine distribution probabilities. Classification maximizes over the probability that a gesture sequence was performed, given the observation data.
Without training, their system's HMM model with 15 states has around 50% accuracy and varies widely. Training can boost the accuracy to around 90%, but training cannot be performed in a real-time environment.
Discussion:
I'm curious as to how long it actually takes the system to train. The axis for the training figure did not specify, and if it only takes 30 seconds to train, this is not much longer than an initial load screen (and it would only have to happen once). If it takes 30 minutes to train, then we have a problem.
Also, the number of gestures in the system would hurt this time factor. Even 10 seconds over 100 gestures is unacceptable.
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1 comment:
I could have read this part of the paper incorrectly, but did it also say that it only achieved around 50% accuracy if the trained system based on their previous user data? It looks like that using their trained HMM doesn't look quite very zesty either.
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