Journal Article10.1162/089976699300016890
Modeling and prediction of human behavior
Alex Pentland,Andrew Liu +1 more
TL;DR: This work proposes that many human behaviors can be accurately described as a set of dynamic models sequenced together by a Markov chain and uses these dynamic Markov models to recognize human behaviors from sensory data and to predict human behaviors over a few seconds time.
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Abstract: We propose that many human behaviors can be accurately described as a set of dynamic modes (e.g., Kalman filters) sequenced together by a Markov chain. We then use 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% accuracy at predicting automobile drivers' subsequent actions from their initial preparatory movements. Language: en
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Citations
A Human Decision-Making Behavior Model for Human-Robot Interaction in Multi-Robot Systems
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A Hybrid Rule-Based and Data-Driven Approach to Driver Modeling Through Particle Filtering
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A Novel Movement Model for Pedestrians Suitable for Personal Navigation
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TL;DR: In this article, a combination of stochastic behavioral movement model and diffusion model for pedestrian navigation is developed and tested using a top-level Markov process to determine whether to currently use the Stochastic behavioral model or diffusion model.
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