Modeling human behavior is important for the design of robots as well as human-computer interfaces that use humanoid avatars. Constructive models have been built, but they have not captured all of the detailed structure of human behavior such as the moment-to-moment deployment and coordination of hand, head and eye gaze used in complex tasks. We show how this data from human subjects performing a task can be used to program a dynamic Bayes network (DBN) which in turn can be used to recognize new performance instances. As a specific demonstration we show that the steps in a complex activity such as sandwich making can be recognized by a DBN in real time.

中文翻译：

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识别手眼协调模式中的行为

对人类行为进行建模对于设计机器人以及使用类人化身的人机界面非常重要。已经建立了建设性的模型，但它们并没有捕捉到人类行为的所有详细结构，例如复杂任务中使用的手、头和眼睛凝视的瞬间部署和协调。我们展示了如何使用来自执行任务的人类受试者的数据来编写动态贝叶斯网络 (DBN)，而动态贝叶斯网络 (DBN) 又可用于识别新的性能实例。作为一个具体的演示，我们展示了复杂活动（如三明治制作）中的步骤可以被 DBN 实时识别。