March 7, 2017

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 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 conscientiously reviewed and chosen from fifty nine submissions. The papers are geared up alongside a collection of thematic tracks: ambient assisted residing; net of items; ambient play and studying; shrewdpermanent constructions and towns; clever riding; facts technological know-how; clever healthcare and therapeutic environments; ambient persuasion; and new and rising themes.

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

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Each localization system is requested to produce localization data with a frequency of 1 sample every half a second. The path includes 3 waiting points, where the actor has to stay still in the same position for 5 seconds. The reference localization system is used to compare the localization data generated by the competitors with the ground truth. The reference consists in a set of pre-defined paths the actor has to follow with a predefined speed. 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.

This filter was applied to each of the 3 acceleration axes. Then, in order to take into account the overall movement of the smartphone the magnitude of the acceleration was computed as follows: M (t) = 2 2 2 ax (t) + ay (t) + az (t) , (2) where ax (t), ay (t) and az (t) are the accelerations at time t for each of the axes. Since each user has its own way of moving (some users tend to move more than others), the magnitude M was standardized (μ = 0 and σ = 1) relative to each user. 3 E. Garcia-Ceja et al.

Confusion matrices when a) using just accelerometer data and b) combining accelerometer and Wi-Fi. 1 means a walking interaction and 0 no interaction. 5 Conclusions and Future Work In this work we showed how accelerometer and Wi-Fi data collected from smartphones can be used to classify walking in synchrony between users. First, we used accelerometer data and Wi-Fi data independently. Our results suggest that using just an accelerometer gives better results than using just Wi-Fi. Then, we combined both sensors and the overall accuracy increased because when adding Wi-Fi it was possible to reduce the false positives due to the fact that the persons may be walking at the same time but in different places.

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