Summary:
Campbell et al. use HMMs and a list of features to find a good recognition rate for a set of T'ai Chi gestures that are performed by users in a swivel chair; a hand gesture's change in polar coordinates provided the highest recognition for the 18 gestures tested.
Discussion:
Performing T'ai Chi in a chair kind of defeats the purpose of T'ai Chi. That's like trying to study race car drivers by observing people who take the bus.
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1 comment:
That, and all they do is shotgun a bunch of feature vectors at a hidden Markov model. It's pretty stupid, even without the chair.
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