We propose that many human behaviors can be accurately described as a set of dynamic models (e.g., Kalman filters) sequenced together gy a Markov chain. We then sue these dynamic Markov models to recognize Human behaviors from sensory data and to predict human behaviors over A few seconds time. To test the power of this modeling approach, we Report an experiment in which we were able to achieve 95 /100 accuracy At predicting automobile dricers's subsequent actions from their initial Preparatory movements.
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