%0 Conference Paper %1 conference09 %A Wimmer, R. %A Kranz, M. %A Boring, S. %A Schmidt, A. %B Proc. Fourth International Conference on Networked Sensing Systems INSS '07 %D 2007 %K CapShelf, CapTable, acquisition acquisition, activity activity-sensing capacitive context data detection, distributed furniture, human-computer interaction, networked recognition, sensors, unobtrusive %P 85--88 %R 10.1109/INSS.2007.4297395 %T CapTable and CapShelf - Unobtrusive Activity Recognition Using Networked Capacitive Sensors %X In this paper, we introduce two pieces of activity-sensing furniture using networked capacitive sensors. CapTable and CapShelf are two example applications for activity detection and context acquisition realized with the CapSensing Toolkit. Both instances are representatives of a greater class of scenarios where networked sensing can compete with other technologies. CapTable is a simple wooden table equipped with capacitive sensors. Hand and body motion can be tracked above and around the table with high resolution. Additionally, conductive and non- conductive objects can be tracked and discriminated. The same features apply to CapShelf, a shelf that can monitor where people are reaching, and partially track the amount of items still in the shelf. We argue, that capacitive sensors provide huge benefits for real-world, privacy-sensitive, and unobtrusive data acquisition and implicit human-computer interaction.

@inproceedings{conference09, abstract = {In this paper, we introduce two pieces of activity-sensing furniture using networked capacitive sensors. CapTable and CapShelf are two example applications for activity detection and context acquisition realized with the CapSensing Toolkit. Both instances are representatives of a greater class of scenarios where networked sensing can compete with other technologies. CapTable is a simple wooden table equipped with capacitive sensors. Hand and body motion can be tracked above and around the table with high resolution. Additionally, conductive and non- conductive objects can be tracked and discriminated. The same features apply to CapShelf, a shelf that can monitor where people are reaching, and partially track the amount of items still in the shelf. We argue, that capacitive sensors provide huge benefits for real-world, privacy-sensitive, and unobtrusive data acquisition and implicit human-computer interaction.}, added-at = {2011-05-31T14:51:31.000+0200}, author = {Wimmer, R. and Kranz, M. and Boring, S. and Schmidt, A.}, biburl = {https://www.bibsonomy.org/bibtex/2621128d40ce8ec030a505dcec5128cd1/matthias.kranz}, booktitle = {Proc. Fourth International Conference on Networked Sensing Systems INSS '07}, doi = {10.1109/INSS.2007.4297395}, interhash = {8d88ecc918f15ba3cfefa723e1409a66}, intrahash = {621128d40ce8ec030a505dcec5128cd1}, keywords = {CapShelf, CapTable, acquisition acquisition, activity activity-sensing capacitive context data detection, distributed furniture, human-computer interaction, networked recognition, sensors, unobtrusive}, pages = {85--88}, timestamp = {2011-05-31T14:51:33.000+0200}, title = {CapTable and CapShelf - Unobtrusive Activity Recognition Using Networked Capacitive Sensors}, year = 2007 }