Modeling and prediction of human behavior
Modeling and prediction of human behavior
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DOI：
10.1162/089976699300016890
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发表时间：
1999-01-01
影响因子：
2.9
通讯作者：
Liu, A
中科院分区：
文献类型：
--
作者：
Pentland, A;Liu, A
我们提出，许多人类行为可以准确地描述为一组动态模型（如。例如，在一个实施例中，卡尔曼滤波器）通过马尔可夫链排序在一起。然后，我们使用这些动态马尔可夫模型从感官数据中识别人类行为，并在几秒钟的时间内预测人类行为。为了测试这种建模方法的能力，我们报告了一个实验，在这个实验中，我们能够达到95%的准确率，从他们最初的准备动作预测汽车司机的后续行动。
We propose that many human behaviors can be accurately described as a set of dynamic models (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.