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Raphael Wimmer, Matthias Kranz, Sebastian Boring, Albrecht Schmidt
CapTable and CapShelf - Unobtrusive Activity Recognition Using Networked Capacitive Sensors
In Proceedings of the Fourth International Conference on Networked Sensing Systems (INSS), Braunschweig, Germany, June 2007 (bib)

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.