Ambient Intelligence: European Conference, AmI 2014, by Emile Aarts, Boris de Ruyter, Panos Markopoulos, Evert van

By Emile Aarts, Boris de Ruyter, Panos Markopoulos, Evert van Loenen, Reiner Wichert, Ben Schouten, Jacques Terken, Rob Van Kranenburg, Elke Den Ouden, Gregory O'Hare

This booklet constitutes the refereed lawsuits of the fifth foreign Joint convention of Ambient Intelligence, AmI 2014, held in Eindhoven, The Netherlands, in November 2014. The 21 revised complete papers offered including five brief papers and four workshop papers have been conscientiously reviewed and chosen from fifty nine submissions. The papers are prepared alongside a collection of thematic tracks: ambient assisted dwelling; net of items; ambient play and studying; shrewdpermanent structures and towns; clever riding; information technology; clever healthcare and therapeutic environments; ambient persuasion; and new and rising themes.

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Extra resources for Ambient Intelligence: European Conference, AmI 2014, Eindhoven, The Netherlands, November 11-13, 2014. Revised Selected Papers

Example text

657. The PPV was low because there were many false positives (See Figure 5). The false positives are due to the fact that users were moving or walking at the same time even though they were not in proximity to each other. Table 2. 997 Now we evaluate the predictions by using just Wi-Fi data. (5). This plot represents the trade off between sensitivity and specificity for different threshold values. 424 since it produces a combination of sensitivity and 1 - specificity that is close to the upper left corner.

For this reason, several systems have been proposed for indoor localization. These algorithms fuse information coming from different sources in order to improve the overall accuracy in monitoring user activities. In literature, each solution has advantages and shortcomings, which, c Springer International Publishing Switzerland 2014 E. Aarts et al. ): AmI 2014, LNCS 8850, pp. 17–32, 2014. 1007/978-3-319-14112-1 2 18 F. Palumbo and P. Barsocchi in most cases, can be summarized in a trade-off between precision and installation complexity (and thus costs).

These measures are summarized in Table 2. 657. The PPV was low because there were many false positives (See Figure 5). The false positives are due to the fact that users were moving or walking at the same time even though they were not in proximity to each other. Table 2. 997 Now we evaluate the predictions by using just Wi-Fi data. (5). This plot represents the trade off between sensitivity and specificity for different threshold values. 424 since it produces a combination of sensitivity and 1 - specificity that is close to the upper left corner.

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