%0 Conference Paper %1 kroll2016implicit %A Kroll, Dennis %A Kusber, Rico %A David, Klaus %B Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct %C Heidelberg, Germany %D 2016 %I ACM %K ComTec SocialLink itegpub %P 732-741 %R 10.1145/2968219.2968280 %T Implicit Positioning Using Compass Sensor Data %U http://doi.acm.org/10.1145/2968219.2968280 %X In this paper, we present Implicit Positioning -- an approach to recognize the indoor area where a person is, such as a specific corridor or corner. Implicit Positioning adapts to its user by automatically finding, learning, and recognizing patterns in data from a smartphone's compass sensor. These patterns model characteristics of the areas the person walked through, such as the direction the person headed, and can be understood as Implicit Positions. Implicit Positioning neither relies on user feedback nor on additional infrastructure, digital maps or war-driving. All the person needs to do is carry a smartphone. To evaluate our approach, we collected data from smartphones carried by a test person in different positions. The test person covered a distance of 17.4 km in two different buildings. When taking a path for the second time, patterns were already being correctly recognized with an accuracy of up to 87.8%. %@ 978-1-4503-4462-3

@inproceedings{kroll2016implicit, abstract = {In this paper, we present Implicit Positioning -- an approach to recognize the indoor area where a person is, such as a specific corridor or corner. Implicit Positioning adapts to its user by automatically finding, learning, and recognizing patterns in data from a smartphone's compass sensor. These patterns model characteristics of the areas the person walked through, such as the direction the person headed, and can be understood as Implicit Positions. Implicit Positioning neither relies on user feedback nor on additional infrastructure, digital maps or war-driving. All the person needs to do is carry a smartphone. To evaluate our approach, we collected data from smartphones carried by a test person in different positions. The test person covered a distance of 17.4 km in two different buildings. When taking a path for the second time, patterns were already being correctly recognized with an accuracy of up to 87.8%.}, acmid = {2968280}, added-at = {2016-10-28T09:15:49.000+0200}, address = {Heidelberg, Germany}, author = {Kroll, Dennis and Kusber, Rico and David, Klaus}, biburl = {https://www.bibsonomy.org/bibtex/2ebbefb8039c0ad8f73766fc3a5751dc0/comtec_pub}, booktitle = {Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct}, doi = {10.1145/2968219.2968280}, interhash = {5aae0654551a7831c3c8b763a84e56d6}, intrahash = {ebbefb8039c0ad8f73766fc3a5751dc0}, isbn = {978-1-4503-4462-3}, keywords = {ComTec SocialLink itegpub}, location = {Heidelberg, Germany}, month = {September}, numpages = {10}, pages = {732-741}, publisher = {ACM}, series = {UbiComp '16}, timestamp = {2016-10-28T09:17:50.000+0200}, title = {Implicit Positioning Using Compass Sensor Data}, url = {http://doi.acm.org/10.1145/2968219.2968280}, year = 2016 }