Work

Learning Situation Models in a Smart Home

Year: 2008

Type: article

Abstract: This paper addresses the problem of learning situation models for providing context-aware services. Context for modeling human behavior in a smart environment is represented by a situation model describing environment, users, and their activities. A framework for acquiring and evolving different layers of a situation model in a smart environment is proposed. Different learning methods are presented as part of this framework: role detection per entity, unsupervised extraction of situations from multimodal data, supervised learning of situation representations, and evolution of a predefined situation model with feedback. The situation model serves as frame and support for the different methods, permitting to stay in an intuitive declarative framework. The proposed methods have been integrated into a whole system for smart home environment. The implementation is detailed, and two evaluations are conducted in the smart home environment. The obtained results validate the proposed approach.

Source: IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)

DOI: 10.1109/tsmcb.2008.923526

Authors: O. Brdiczka, J.L. Crowley, P. Reignier

Institutions: Palo Alto Research Center, Institut polytechnique de Grenoble, Institut national de recherche en sciences et technologies du numérique, Université Joseph Fourier

Language: English

Topic: Context-Aware Activity Recognition Systems

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2166472401

FWCI: 7.241

Cites: 9

Cited by: 182

Related to: 10