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Human State Estimation Through Learning Over Common Sense Data

posted Sep 2, 2008, 10:34 AM by Brian Tanner   [ updated Sep 2, 2008, 11:00 AM ]

William Pentney & Matthai Philipose & Jeff Bilmes & Henry Kautz

We seek to tackle the problem of human state recognition, in which sensor-based observations are used to reason about the state of the general
human environment. Recent work [Pentney et al., 2006] has shown promise in using large publicly available hand-contributed commonsense databases as joint models that can be used to interpret day-to-day ob ject-use data. We discuss the development of a graphical model for reasoning over large amounts of commonsense information about human activity, and the use of Web-based information retrieval techniques to evaluate and enhance such information for more effective use. The large scale of this commonsense data creates issues of scale in inference over our graphical model; we present some means of efficiently performing inference over such a model. Additionally, we discuss how to improve the performance of our model through the use of learning techniques which can scale to the very large networks induced by this commonsense data. Finally, we present experiments to show how these techniques can be used to provide improved results in the prediction of everyday human state.