By Yuanping Li, Ling Feng, Lin Qiao, Yiping Li, Shoubin Kong, Yu Yi, Daqing Zhang (auth.), Paul Lukowicz, Kai Kunze, Gerd Kortuem (eds.)
This quantity constitutes the revised papers of the fifth eu convention on shrewdpermanent Sensing and Context, EuroSSC 2010, held in Passau, Germany, in November 2010. The thirteen revised complete papers have been conscientiously reviewed and chosen from various submissions. The papers deal with themes similar to purposes; sensing; platforms help; and better point modeling. as well as the convention is a quick file in regards to the Workshop on Ambient Assisted dwelling (AAL) Platform integrated.
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Additional resources for Smart Sensing and Context: 5th European Conference, EuroSSC 2010, Passau, Germany, November 14-16, 2010. Proceedings
Notice that in contrast to the previous plots of case 1 and 2 no unclassiﬁed posture is detected, leaving no whitespaces in the plot. By training a classiﬁer on more than one night the precision is increased to 88%, whereas recall stays steady in the range of 80% (see ﬁgure 6, right plot). How to Log Sleeping Trends? 25 Using only an artiﬁcial dataset is not feasible due to user-speciﬁc postures and is therefore not further followed. 3 Combining Both Modalities We have shown that both systems can be operated individually to detect sleeping postures.
17(3), 336–346 (2007) 12. : Agents in delivering personalized content based on semantic metadata. In: Proceedings of AAAI Spring Symposium Workshop on Intelligent Agents in Cyberspace, pp. 84–93 (1999) Utilizing Social Context for Providing Personalized Services to Mobile Users 41 13. : Printing in ubiquitous computing environments. , Indulska, J. ) UIC 2009. LNCS, vol. 5585, pp. 83–98. Springer, Heidelberg (2009) 14. : A presence event package for the session initiation protocol (SIP). RFC 3856, Internet Engineering Task Force (August 2004) 15.
24 H. Becker, M. Borazio, and K. Van Laerhoven ground truth artificial set 1 training set 4 training sets Fig. 5. Detected postures with increasing amount of training sets 90 80 precision recall 70 60 [%] 50 40 30 20 10 0 artificial set 1 day training 4 days training Fig. 6. Precision and recall for all three cases Case 2: 1 training set. To compare the ﬁrst case to a diﬀerent approach, in the second case a k-nearest-neighbor (KNN) classiﬁer is trained on one night only. The classiﬁer is then tested on the four other nights and compared to the ground truth.
Smart Sensing and Context: 5th European Conference, EuroSSC 2010, Passau, Germany, November 14-16, 2010. Proceedings by Yuanping Li, Ling Feng, Lin Qiao, Yiping Li, Shoubin Kong, Yu Yi, Daqing Zhang (auth.), Paul Lukowicz, Kai Kunze, Gerd Kortuem (eds.)