Ambient Intelligence

Remagnino, Paolo; Hagras, Hani; Monekosso, Dorothy; Velastín, Sergio A.

doi:10.1007/0-387-22991-4_1

Cited by 21 publications

(10 citation statements)

References 5 publications

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“…Thanks to that, user friendliness, user empowerment, human assistance, and easy interaction with efficient services are improved. Examples of AmI scenarios can be seen in Augusto [2007], Remagnino et al [2005], and .…”

Section: Ambient Intelligencementioning

confidence: 99%

“…AmI systems are usually composed by at least (a) a perception mechanism to gather information from both the user and the environment [Remagnino et al 2005], (b) a set of actuators to modify the environment and communicate with users, and (c) a reasoning/decision-making module able to recognize what is happening to users in the environment, what they do, and what their aims are and to make decisions to assist them. These three abstract components often expand when designing AmI applications, since some scenarios also require the design of a sensor network, data fusion techniques, or a real-time response, among others [Remagnino et al 2005;Ramos et al 2008].…”

Section: Ambient Intelligencementioning

confidence: 99%

“…Thus, it becomes necessary to develop techniques to model, learn, recognize, and predict what users are doing in the environment, so that the system is able to make decisions about how to assist them. Usually, the literature calls what users are doing human behavior or human activity interchangeably [Remagnino et al 2005;; Rashidi and Cook 2009;Ros et al 2013]. These terms usually mean a sequence of human actions that can be tagged with a label, that is, the corresponding activity/behavior.…”

Section: Ambient Intelligencementioning

confidence: 99%

See 2 more Smart Citations

A survey on ontologies for human behavior recognition

et al. 2014

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Describing user activity plays an essential role in ambient intelligence. In this work, we review different methods for human activity recognition, classified as data-driven and knowledge-based techniques. We focus on context ontologies whose ultimate goal is the tracking of human behavior. After studying upper and domain ontologies, both useful for human activity representation and inference, we establish an evaluation criterion to assess the suitability of the different candidate ontologies for this purpose. As a result, any missing features, which are relevant for modeling daily human behaviors, are identified as future challenges.

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Section: Ambient Intelligencementioning

confidence: 99%

Section: Ambient Intelligencementioning

confidence: 99%

Section: Ambient Intelligencementioning

confidence: 99%

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A survey on ontologies for human behavior recognition

et al. 2014

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“…One area benefited by the appearance of these new devices is "Ambient Intelligence" (Remagnino et al, 2005). This area attempts to integrate information technology in any personal environment, facilitating daily activities as transparently as possible with respect to the information systems.…”

Section: Introductionmentioning

confidence: 99%

A Component-based User Interface Approach for Smart TV

Vallecillos

Criado

Padilla

et al. 2014

Proceedings of the 9th International Conference on Software Engineering and Applications

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Abstract:The fast growth and diversity of technological devices currently being produced is benefiting areas such as "ambient intelligence". This area attempts to integrate information technology in any personal environment. However, to construct service/application software that adapts to different environments, there must be techniques available that favor this type of development. Component-based software Engineering (CBSE) is a discipline of the software engineering that integrates (previously constructed) components to build new software systems. This paper presents a CBSE approach to build Graphical User Interfaces (GUI) at run-time. Both a component-based perspective of the user interface and a set of component relationships are presented in the paper. As a case study, this paper also describes an application built for an emerging computation environment, Smart TV. A running example is also presented through the paper putting some Web-based solutions to build User Interfaces together (e.g., Wookie, W3C Widgets, Node.js).

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“…Ambient intelligence systems allow to enhance and improve the users activity in an environment (Remagnino, Foresti, and Ellis 2005;Weber Rabaey, and Aarts 2005) by the cooperation with devices which have different levels of interaction capabilities. Devices can realize some ambient specific tasks by delegation (i.e., the device can acquire some user sub goals and realize them), or by means of reactive behavior (i.e., to guard some safety conditions, or to provide some basic service).…”

Section: Introductionmentioning

confidence: 99%

Planning in Reactive Environments

Milani

Poggioni

2007

Computational Intelligence

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The diffusion of domotic and ambient intelligence systems have introduced a new vision in which autonomous deliberative agents operate in environments where reactive responses of devices can be cooperatively exploited to fulfill the agent's goals. In this article a model for automated planning in reactive environments, based on numerical planning, is introduced. A planner system, based on mixed integer linear programming techniques, which implements the model, is also presented. The planner is able to reason about the dynamic features of the environment and to produce solution plans, which take into account reactive devices and their causal relations with agent's goals by exploitation and avoidance techniques, to reach a given goal state. The introduction of reactive domains in planning poses some issues concerning reasoning patterns which are briefly depicted. Experiments of planning in reactive domains are also discussed.

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Ambient Intelligence

Cited by 21 publications

References 5 publications

A survey on ontologies for human behavior recognition

A survey on ontologies for human behavior recognition

A Component-based User Interface Approach for Smart TV

Planning in Reactive Environments

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