Ambient Intelligence: European Conference, AmI 2007, by Enrico Rukzio, Gregor Broll, Karin Leichtenstern, Albrecht

By Enrico Rukzio, Gregor Broll, Karin Leichtenstern, Albrecht Schmidt (auth.), Bernt Schiele, Anind K. Dey, Hans Gellersen, Boris de Ruyter, Manfred Tscheligi, Reiner Wichert, Emile Aarts, Alejandro Buchmann (eds.)

This booklet constitutes the refereed complaints of the 1st ecu convention on Ambient Intelligence, AmI 2007, held in Darmstadt, Germany, in November 2007.

The 17 revised complete papers provided including 5 learn reviews have been rigorously reviewed and chosen from forty eight submissions. The papers are equipped in topical sections on mobility and sensing, functions of AmI, job and site sensing, AmI and synthetic intelligence, AmI middleware and infrastructure, and interplay with the surroundings.

The e-book is rounded off through a piece on case stories and classes realized, offering a excessive point number of present study experiences and papers that mirror crucial actions of present ambient intelligence research.

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Additional info for Ambient Intelligence: European Conference, AmI 2007, Darmstadt, Germany, November 7-10, 2007. Proceedings

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A sensor was marked as correctly detecting an object usage event if it activated at least once when the object was being manipulated. For example, a drawer manipulation that is a quick open and shut would be counted as one movement while manipulation consisting of an open, release, pause, and then grab and close would be counted as two usage events. The number of FP activations induced in sensors by the usage of objects nearby the object being used was also computed. Over the course of the four hours, the annotator found 327 distinct object manipulations.

The main purpose of the objectusage detection algorithm that runs on the sensor node is to recognize when an object is being used by analyzing the object’s acceleration signal while minimizing false positives due to adjacent object manipulation and noise. g. ethnographers), and (3) minimizing potential problems when pattern classification or machine learning algorithms are used to infer context from object usage. The first step of the object usage detection algorithm consists of preprocessing the acceleration signals over each axis (X, Y) by computing the absolute value of the difference between adjacent samples to achieve orientation invariance.

Intille, and K. Larson up from the couch is also difficult to recognize because of wireless signal loss due to body blocking. Putting down a mug on a table was difficult to recognize because it generates short duration signals similar to the ones generated by adjacencies. Finally, the number of FPs is high for worse-case conditions such as slamming events or inevitable adjacent motion. For example, sitting on the center cushion of a three-seat couch induced significant motion on the two adjacent cushions.

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