Layered representations for learning and inferring office activity from multiple sensory channels

We present the use of layered probabilistic representations for modeling human activities, and describe how we use the representation to do sensing, learning, and inference at multiple f temporal granularity and abstraction and from heterogeneous data sources. The approach centers on the use of a cascade of Hidden Markov Models named Layered Hidden Markov Models (LHMMs) to diagnose states of a user

• Type: Journal
• Year: 2004
• Where: CVIU
• Authors: Nuria Oliver, Ashutosh Garg, Eric Horvitz
• Keywords: CVIU 2004, Heterogeneous Data Sources, Hidden Markov Models, Markov Models