Layered Representations for Recognizing Office Activity

Nuria Oliver, Eric Horvitz, Ashutosh Garg

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We present the use of layered probabilistic representations using Hidden Markov Models for performing sensing, learning, and inference at multiple levels of temporal granularity. We describe the use of the representation in a system that diagnoses states of a user's activity based on real-time streams of evidence from video, acoustic, and computer interactions. We review the representation, present an implementation, and report on experiments with the layered representation in an office-awareness application.

Keywords: Human activity recognition, office awareness, Hidden Markov Models (HMMs), Layered HMMs, multimodal systems, context awareness, attentional user interfaces

In: Proceedings of the Fourth IEEE International Conference on Multimodal Interaction, Pittsburgh, PA, October 2002, pp. 3-8.

Images from Seer debut at keynote presentation by Bill Gates, IJCAI 2001. Eric Horvitz presents the Priorities and Notification Platform projects and then introduces Ashutosh Garg and Nuria Oliver for a demonstration of the Seer context-sensing system.

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