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HMM parameter reduction for practical gesture recognition

We examine in detail some properties of gesture recognition models which utilize a reduced number of parameters and lower algorithmic complexity compared to traditional hidden Markov models. We show that the reduced parameter models are comparable to standard HMM-based gesture recognition models in their ability to effectively model gestures, and in some cases superior when training data is limited. We also show that in order to effectively differentiate similar gestures, a gesture recognition model must utilize a large number of states, a scenario which can only be adequately handled by reducer parameter methods to maintain real-time speeds.