Layered Representations for Human Activity Recognition

Nuria Oliver, Eric Horvitz, Ashutosh Garg

Abstract: 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 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.