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This publication constitutes the refereed complaints of the fifth overseas Joint convention of Ambient Intelligence, AmI 2014, held in Eindhoven, The Netherlands, in November 2014. The 21 revised complete papers provided including five brief papers and four workshop papers have been rigorously reviewed and chosen from fifty nine submissions. The papers are equipped alongside a collection of thematic tracks: ambient assisted dwelling; web of items; ambient play and studying; clever structures and towns; clever using; info technological know-how; clever healthcare and therapeutic environments; ambient persuasion; and new and rising themes.
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Additional resources for Ambient Intelligence: European Conference, AmI 2014, Eindhoven, The Netherlands, November 11-13, 2014. Revised Selected Papers
Robust Environmental Sound Recognition for Home Automation. IEEE Transactions on Automation Science and Engineering 5(1), 25–31 (2008) 22. : Ambient Intelligence. Springer (March, 2005) 23. : Training of Classifiers for the Recognition of Musical Instrument Dominating in the Same-Pitch Mix. it Abstract. Localization is a key component for many AAL systems, since the user position can be used for detecting user’s activities and activating devices. While for outdoor scenarios Global Positioning System (GPS) constitutes a reliable and easily available technology, in indoor scenarios, in particular in real homes, GPS is largely unavailable.
Indoor localization systems can be classiﬁed based on the signal types and/or technologies (infrared, ultrasound, ultra wideband, RFID, packet radio), signal metrics (AOA angle of arrival, TOA time of arrival, TDOA time diﬀerence of arrival, and RSS received signal strength), and the metric processing methods (range-based and range free algorithms) . ). Data fusion techniques may be used to integrate the information obtained from diﬀerent sensor sources [10–12] in order to reduce the localization error.
Org 2 Tecnol´ ogico de Monterrey, Campus Monterrey, Av. , M´exico Abstract. Social interactions play an important role in the overall well-being. Current practice of monitoring social interactions through questionnaires and surveys is inadequate due to recall bias, memory dependence and high end-user eﬀort. However, sensing capabilities of smart-phones can play a signiﬁcant role in automatic detection of social interactions. In this paper, we describe our method of detecting interactions between people, speciﬁcally focusing on interactions that occur in synchrony, such as walking.