Touche Object Sensing

Touche is a touch interaction technology that uses capacitive sensing in a new way to create a variety of interactive experiences with everyday objects. Using a frequency scanning technique allows it to detect complex touch configurations and create profiles of many contact and gesture forms. These profiles allow many forms of interaction with devices and objects.

Revolutionary technology enables objects to know how they are being touched – [cmu.edu]

A doorknob that knows whether to lock or unlock based on how it is grasped, a smartphone that silences itself if the user holds a finger to her lips and a chair that adjusts room lighting based on recognizing if a user is reclining or leaning forward are among the many possible applications of Touché, a new sensing technique developed by a team at Disney Research, Pittsburgh, and Carnegie Mellon University.

Touché is a form of capacitive touch sensing, the same principle underlying the types of touchscreens used in most smartphones. But instead of sensing electrical signals at a single frequency, like the typical touchscreen, Touché monitors capacitive signals across a broad range of frequencies.

This Swept Frequency Capacitive Sensing (SFCS) makes it possible to not only detect a “touch event,” but to recognize complex configurations of the hand or body that is doing the touching. An object thus could sense how it is being touched, or might sense the body configuration of the person doing the touching.

Touché: Enhancing Touch Interaction on Humans, Screens, Liquids, and Everyday Objects – [youtube.com]

Touché proposes a novel Swept Frequency Capacitive Sensing technique that can not only detect a touch event, but also recognize complex configurations of the human hands and body. Such contextual information significantly enhances touch interaction in a broad range of applications, from conventional touchscreens to unique contexts and materials. For example, in our explorations we add touch and gesture sensitivity to the human body and liquids. We demonstrate the rich capabilities of Touché with five example setups from different application domains and conduct experimental studies that show gesture classification accuracies of 99% are achievable with our technology.

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