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

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

ISBN-10: 3319141112

ISBN-13: 9783319141114

ISBN-10: 3319141120

ISBN-13: 9783319141121

This e-book constitutes the refereed complaints of the fifth foreign 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 equipped alongside a collection of thematic tracks: ambient assisted residing; web of items; ambient play and studying; clever structures and towns; clever riding; info technological know-how; shrewdpermanent healthcare and therapeutic environments; ambient persuasion; and new and rising themes.

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

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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.

AmI 2014, LNCS 8850, pp. 33–46, 2014. 1007/978-3-319-14112-1 3 34 E. Garcia-Ceja et al. The current practice of using surveys, diaries and self reporting methods to record social interactions has several drawbacks such as recall bias, memory dependence, and high end-user effort for continuous long term monitoring [5]. Also, many serious mental illness (SMI) require lifelong management and individuals need to constantly look for signs that might indicate relapse [6], which is infeasible with manual methods.

This is because there is no threshold value in the ROC curve that produces a combination of both sensitivity and specificity that are greater or equal to the ones obtained when using accelerometer data. Fig. 6. Resulting ROC curve for different threshold values of the BSSIDs variance Finally, we performed the prediction by combining both, accelerometer and Wi-Fi data. Many of the false positives are there because the users may be walking at the same time even though they are in different places. (5) to compute the distance between the control user and the test user at any given time slot.

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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


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