DocumentCode :
2969754
Title :
Layered representations for human activity recognition
Author :
Oliver, Nuria ; Horvitz, Eric ; Garg, Ashutosh
Author_Institution :
Adaptive Syst. & Interaction, Microsoft Res., Redmond, WA, USA
fYear :
2002
fDate :
2002
Firstpage :
3
Lastpage :
8
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.
Keywords :
acoustic signal processing; hidden Markov models; inference mechanisms; learning (artificial intelligence); office automation; real-time systems; sensor fusion; user interfaces; video signal processing; acoustic interactions; computer interactions; hidden Markov models; human activity recognition; inference; layered probabilistic representations; learning; office-awareness application; real-time evidence streams; sensing; temporal granularity; video interactions; Adaptive systems; Application software; Context; Hidden Markov models; Humans; Machinery; Real time systems; Streaming media; Surveillance; Traffic control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimodal Interfaces, 2002. Proceedings. Fourth IEEE International Conference on
Print_ISBN :
0-7695-1834-6
Type :
conf
DOI :
10.1109/ICMI.2002.1166960
Filename :
1166960
Link To Document :
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