Download Ambient Intelligence: Third International Joint Conference, by Hristijan Gjoreski, Mitja Luštrek, Matjaž Gams (auth.), PDF

By Hristijan Gjoreski, Mitja Luštrek, Matjaž Gams (auth.), Fabio Paternò, Boris de Ruyter, Panos Markopoulos, Carmen Santoro, Evert van Loenen, Kris Luyten (eds.)

This publication constitutes the refereed complaints of the 3rd overseas Joint convention an Ambient Intelligence, AmI 2012, held in Pisa, Italy, in November 2012. The 18 revised complete papers and five brief papers offered have been rigorously reviewed and chosen from forty seven (full papers) respectively 14 (short papers) submissions. From a systematic viewpoint, the papers make a multidisciplinary process protecting fields like desktop technology, human laptop interplay, electric engineering, business layout, behavioral sciences, geared toward enriching actual environments with a community of allotted units, corresponding to sensors, actuators, and computational assets, so one can help clients of their daily actions. From a technological viewpoint the quantity represents the convergence of modern achievements in ubiquitous and verbal exchange applied sciences, pervasive computing, clever consumer interfaces and synthetic intelligence.

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Additional resources for Ambient Intelligence: Third International Joint Conference, AmI 2012, Pisa, Italy, November 13-15, 2012. Proceedings

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Borazio 22. : ADL Recognition Based on the Combination of RFID and Accelerometer Sensing. In: Proceedings of the 2nd International Conference on Pervasive Computing Technologies for Healthcare (Pervasive Health 2008), pp. 258–263. IEEE Xplore, Tampere (2008) 23. : Activity recognition of assembly tasks using body-worn microphones and accelerometers. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(10), 1553–1567 (2006) 24. : A Capacitive Sensing Toolkit for Pervasive Activity Detection and Recognition.

6%. 5% respectively. The low performance results from confusions with other activities, with a higher tendency to the “make sandwich” activity across all participants. This is most likely due to various objects involved in both activities, and the fact that 1-second features are obtained. The capacitive sensor modality has a more positive impact reducing the confusion with other activities. Figure 9 shows an example instance of preparing a bread with marmalade. It is notable that the acceleration data does not seem to provide any characteristic patterns, while the proximity sensor indicates a table, plate, or other objects make sandwich capacitive sensor 64300 64200 64100 64000 3D acceleration 63900 250 200 150 100 50 00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 time (seconds) Fig.

5ms. number of charging cycles in a given time span. In order to guard the sensor from measuring the capacitance to underlying objects, a shield electrode is placed directly underneath the measuring electrode. The shield is driven with the same potential as the sensing electrode, such that the capacitance between the two electrodes is negligible. Using this shielding method, the measured capacitance will only be slightly affected by the grounded underlying arm. Figure 4 shows the the wrist-worn prototype used in the evaluation experiments, with the HedgeHog as the main data logger, the capacitive sensor circuit and the wristband holding the sensing and shielding electrodes.

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