By Martijn H. Vastenburg, Thomas Visser, Marieke Vermaas, David V. Keyson (auth.), Emile Aarts, James L. Crowley, Boris de Ruyter, Heinz Gerhäuser, Alexander Pflaum, Janina Schmidt, Reiner Wichert (eds.)
This booklet constitutes the refereed court cases of the second one eu convention on Ambient Intelligence, AmI 2008, held in Nuremberg, Germany, in November 2008.
The 22 revised complete papers provided have been conscientiously reviewed and chosen from various submissions for inclusion within the booklet. The papers are approximately categorized into teams on healthiness and care, and mobility and logistics respectively; they're equipped in topical sections on service-oriented clever and cozy houses for higher sport and rest time, clever and cooperative family units for clever domestic and purchasing environments, navigation and advice in unknown environments and strange events, context-oriented health and wellbeing tracking and alerting structures for a carefree life.
Read Online or Download Ambient Intelligence: European Conference, AmI 2008, Nuremberg, Germany, November 19-22, 2008. Proceedings PDF
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Additional info for Ambient Intelligence: European Conference, AmI 2008, Nuremberg, Germany, November 19-22, 2008. Proceedings
The user model thereby controls the intelligent communication between the user and his virtual doppelganger. Human services extend the role of humans in AmI environments and lead to the proposed user integration at service level which is illustrated in ﬁgure 5. In consequence the user is no longer considered to be only the trigger of system activities but, instead, the functionality of the environment is enhanced by the user’s skills. Functional dependencies at hardware level as discussed above are dramatically reduced by human services.
A) Surprise expression. (b) Anger expression. (c) Joy expression. on the learned examples. We have used three diﬀerent classiﬁcation schemes: i) Linear Discriminant Analysis, ii) Non-parametric Discriminant Analysis, and iii) Support Vector Machines with a Radial Basis Function. 18 B. Raducanu and F. Dornaika It is worth noting that the static recognition scheme will use the facial actions associated with only one single frame. In this case, the training examples correspond to the apex of the expression or to its half apex.
Major: train with apex frames and test with half-apex. 0000% Table 5. 1428% This result may have very practical implications assuming that training data contain non-apex expressions, specially for real-world applications. In humancomputer interaction scenarios, for instance, we are interested in quantifying Dynamic vs. Static Recognition of Facial Expressions 21 Table 6. 4285% Table 7. SVM - Cross-check validation results for the static classiﬁer. Minor: train with half-apex frames and test with apex.
Ambient Intelligence: European Conference, AmI 2008, Nuremberg, Germany, November 19-22, 2008. Proceedings by Martijn H. Vastenburg, Thomas Visser, Marieke Vermaas, David V. Keyson (auth.), Emile Aarts, James L. Crowley, Boris de Ruyter, Heinz Gerhäuser, Alexander Pflaum, Janina Schmidt, Reiner Wichert (eds.)