February 22, 2017

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

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 court cases 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 conscientiously reviewed and chosen from forty seven (full papers) respectively 14 (short papers) submissions. From a systematic viewpoint, the papers make a multidisciplinary process overlaying fields like machine technology, human desktop interplay, electric engineering, commercial layout, behavioral sciences, aimed toward enriching actual environments with a community of disbursed units, comparable to sensors, actuators, and computational assets, with the intention to aid clients of their daily actions. From a technological standpoint the amount represents the convergence of contemporary achievements in ubiquitous and communique applied sciences, pervasive computing, clever consumer interfaces and synthetic intelligence.

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

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For other users, ”make sandwich” was confused with ”eating” or ”drinking”. Using the new input modality, confusion across users could be reduced. When considering the eating activity, the impact of the new capacitive proximity sensor on the classification performance is quite low, as the chosen features are able to distinguish it from other activities. 6% with the proximity data. An example of an eating activity is shown in Figure 10, where the participant took a few bites from the sandwich while putting it down every time.

13. During the sleeping activity the data from both sensors remains constant for large time spans. The capacitive sensor plot shows the coverage of the arm with either cushions, blankets or the proximity to the mattress, other parts of the bed, the head or body of the participant. without proximity data with proximity data a b c d e f g h i a b c d e f g h i 8 0 0 0 0 0 0 2 1 11 0 0 2 1 0 0 0 1 0 140 0 5 2 0 0 0 0 0 140 0 5 2 0 0 0 0 0 0 294 1 9 10 1 1 0 0 0 309 1 0 4 0 0 1 1 7 1 50 17 9 1 4 1 2 6 0 67 14 9 1 2 1 0 2 7 7 102 42 5 0 2 1 1 0 13 120 24 7 1 1 0 0 3 5 21 288 4 0 0 0 0 2 1 19 300 3 0 0 0 5 11 1 29 40 18 0 0 0 4 4 0 26 57 16 0 0 2 0 0 2 0 0 0 11 0 2 0 0 0 0 0 0 14 0 0 2 2 0 2 0 0 0 271 0 0 0 0 1 0 0 0 276 ← classified as a = open door b = sitting c = lying d = get things e = make sandwich f = eating g = drinking h = walking i = sleeping Fig.

20 40 60 time (seconds) 80 100 120 Fig. 7. Example of the “sitting” activity in which the user moved his hands quite frequently (bottom plot). Most of the time the values of the proximity sensor stay more or less constant, probably due to the hands position on the couch’s fabric. The sharp peak in the capacitive sensor data (upper plot) occurred when the participant scratched the back of his head. In the following, the participants were instructed to lie down on the couch. Again, there were great variations in how this activity was performed by the participants.

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