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Proceedings ArticleDOI

Embedding Self-Awareness into Objects of Daily Life -- The Smart Kettle

TLDR
The concept of embedded self-aware profiles for smart devices that can be used to gain a deeper contextual understanding of their use are discussed and the emergence of a general model of Ambient Intelligence that is based on the collective existence and behavior of such smart devices is discussed.
Abstract
Intelligent Environments on varying scales and for different purposes are slowly becoming a reality. In the near future, global smart world infrastructures will become a commodity that will support various activities of daily life at different degrees of realism. Such infrastructures have the potential to offer dedicated, context- and situation-aware information and services by simultaneously providing the next-generation of data collection, execution and service provisioning layers. One key aspect of this vision is the correct monitoring and understanding of how people interact with their environment; how they can actually benefit from the added intelligence; and finally how future services can be improved or better personalized to enhance human environment interaction as a whole. This level of intelligence is of particular relevance in the health and social care domain where person-centric services can be deployed to assist or even enable a person in performing activities of daily living. This paper discusses the concept of embedded self-aware profiles for smart devices that can be used to gain a deeper contextual understanding of their use and also discusses the emergence of a general model of Ambient Intelligence that is based on the collective existence and behavior of such smart devices. Although generic in principle, the proposed concepts have been exemplified by a distinct use case, namely a smart kettle.

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Citations
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Proceedings ArticleDOI

Towards an Analysis Framework of Technology Habituation by Older Users

TL;DR: In this paper, an analytic framework based on an analysis of related research on appropriation and habituated objects is proposed to identify steps in appropriation from inspection to performance and habituation of an augmented habituated object, a messaging kettle.
Proceedings ArticleDOI

Towards Intelligent and Self-Evolving Network Infrastructures for Energy Management

TL;DR: Some of the requirements of next-generation energy grids are discussed and some of the tasks and challenges in this area that need to be addressed in order to realize infrastructures that are fully intelligent and are also aware of their social and economic parameters so that energy usage can be dynamically and autonomously controlled in relation to global demand and supply.
Proceedings ArticleDOI

Cognitive sensor networks: Towards self-adapting ambient intelligence for pervasive healthcare

TL;DR: In this article, the potential and challenges of future sensor networks that provide the foundations for self-adapting ambient intelligence and briefly discusses some of the requirements thereof are discussed, as well as the application and services in the area of pervasive computing in healthcare and beyond.
Proceedings ArticleDOI

Introducing a new method for temperature measurement by magnetostrictive position sensors

TL;DR: A method for temperature measurement is described, based on the highly temperature dependent reflections of the ultrasonic signal at the outer end of the MS waveguide, where a wave damping material is placed by design.

Cognitive Sensor Networks: Towards Self-Adapting Ambient Intelligence for Pervasive Healthcare

TL;DR: The potential and challenges of future sensor networks that provide the foundations for self-adapting ambient intelligence are sketched and some of the requirements thereof are discussed.
References
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Journal ArticleDOI

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Journal ArticleDOI

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Proceedings ArticleDOI

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Proceedings ArticleDOI

Smart-Its Friends: A Technique for Users to Easily Establish Connections between Smart Artefacts

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Proceedings Article

Sensor-based understanding of daily life via large-scale use of common sense

TL;DR: This work shows how to use the 50,000-fact hand-entered Open-Mind Indoor Common Sense database to interpret sensor traces of day-to-day activities with 88% accuracy and 32/53% precision/recall.
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