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The authors present their "lived-with" experience with an end-user development (EUD) prototype deployed in their home and show how the results overlap and complement findings from more traditional approaches to the study of EUD for the home. This article is part of a special issue on domestic pervasive computing. [ABSTRACT FROM AUTHOR]

This paper addresses learning and recognition of human behavior models from multimodal observation in a smart home environment. The proposed approach is part of a framework for acquiring a high-level contextual model for human behavior in an augmented environment. A 3-D video tracking system creates and tracks entities (persons) in the scene. Further, a speech activity detector analyzes audio streams coming from head set microphones and determines for each entity, whether the entity speaks or not. An ambient sound detector detects noises in the environment. An individual role detector derives basic activity like "walking" or "interacting with table" from the extracted entity properties of the 3-D tracker. From the derived multimodal observations, different situations like "aperitif" or "presentation" are learned and detected using statistical models (HMMs). The objective of the proposed general framework is two-fold: the automatic offline analysis of human behavior recordings and the online detection of learned human behavior models. To evaluate the proposed approach, several multimodal recordings showing different situations have been conducted. The obtained results, in particular for offline analysis, are very good, showing that multimodality as well as multiperson observation generation are beneficial for situation recognition. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Presents the intelligent environment SmartOffice developed by Gravir-IMAG Lab. Anticipation of user intentions through user monitoring; Augmentation of the environment to communicate useful information; Testbed for collaborating and combining independent modules into a single coherent application. INSET: Motivation and Background..