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 court cases of the fifth overseas Joint convention of Ambient Intelligence, AmI 2014, held in Eindhoven, The Netherlands, in November 2014. The 21 revised complete papers awarded including five brief papers and four workshop papers have been conscientiously reviewed and chosen from fifty nine submissions. The papers are equipped alongside a collection of thematic tracks: ambient assisted dwelling; net of items; ambient play and studying; clever constructions and towns; clever riding; facts technology; clever healthcare and therapeutic environments; ambient persuasion; and new and rising themes.
Read Online or Download Ambient Intelligence: European Conference, AmI 2014, Eindhoven, The Netherlands, November 11-13, 2014. Revised Selected Papers PDF
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Extra info for Ambient Intelligence: European Conference, AmI 2014, Eindhoven, The Netherlands, November 11-13, 2014. Revised Selected Papers
The Living Lab’s ﬂoor was covered with marks (with diﬀerent colors to distinguish the right and left foot) that indicate each single step the actor has to follow. Moreover, the actor was synchronized by a digital metronome that indicates the right cadence (one beep for each step), guaranteeing the repeatability of the test. 25 2 4 6 e [m] (e) 8 10 12 14 0 2 4 6 e [m] 8 10 12 14 (f) Fig. 5. CDFs comparison of the competitors localization systems error with and without the application of the SALT overlay applied.
E. LOCOSmotion and AmbiTrack) improve their accuracy of about 13% and 24% respectively. Moreover, from these results we can also conclude that, although the binary information reduces the localization error, the choice and the design of a localization algorithm is the most important issue. In fact, the ranking in terms of accuracy remains unchanged (see Table 2) except for the IPNLas and Magsys systems that, increasing the accuracy of about 36% and 33% respectively, would have had a better score.
Table 3. 772). Figure 7 shows the resulting confusion matrices when using just accelerometer data and when using accelerometer with Wi-Fi data. We can see that the second column of the matrices remained the same. The ﬁrst columns of the matrices had a signiﬁcant change because the false positives where reduced considerably by ruling out detected interactions from accelerometer data that were not real because the users were in diﬀerent places. Detecting Walking in Synchrony Through Smartphone Accelerometer 43 Fig.
Ambient Intelligence: European Conference, AmI 2014, Eindhoven, The Netherlands, November 11-13, 2014. Revised Selected Papers 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