A new pattern matching method, the partly-hidden Markov model, is proposed for gesture recognition. The hidden Markov model, which is widely used for the time series pattern recognition, can deal with only piecewise stationary stochastic process. We solved this problem by introducing the modified second order Markov model, in which the first state is hidden and the second one is observable. As shown by the results of 6 sign-language recognition test, the error rate was improved by 73% compared with normal HMM.
['Tetsunori Kobayashi', 'Satoshi Haruyama']
Partly-hidden Markov model and its application to gesture recognition