CapTable and CapShelf - Unobtrusive activity recognition using networked capacitive sensors

In this paper we introduce two pieces of activity-sensing furniture using networked capacitive sensors. Cap Table and CapShelf are two example applications for activity detection and context acquisition realized with the CapSensing Toolkit [1]. 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.