Swept frequency capacitive touch sensing

Touché is a novel capacitive touch sensing technology that provides rich touch and gesture sensitivity to a variety of analogue and digital objects. The technology is scalable, i.e., the same sensor is equally effective for a pencil, a doorknob, a mobile phone or a table. Sensing with Touché is not limited to inanimate objects –the user’s body can also be made touch and gesture sensitive (Figures 1a ). In general, Touché makes it very easy to add touch and gesture interactivity to unusual, nonsolid objects and materials, such as a body of water.

Notably, instrumenting objects, humans and liquids withTouché is trivial: a single electrode embedded into an objectand attached to our sensor controller is sufficient to computationallyenhance an object with rich touch and gestureinteractivity. Furthermore, in the case of conductive objects,e.g., doorknobs or a body of water, the object itself acts asan intrinsic electrode – no additional instrumentation is necessary.Finally, Touché is inexpensive, safe, low power andcompact; it can be easily embedded or temporarily attachedanywhere touch and gesture sensitivity is desired.

Touché proposes a novel form of capacitive touch sensing that iscalled Swept Frequency Capacitive Sensing (SFCS).In a typical capacitive touch sensor, a conductive object isexcited by an electrical signal at a fixed frequency. Thesensing circuit monitors the return signal and determinestouch events by identifying changes in this signal caused bythe electrical properties of the human hand touching theobject.In SFCS, on the other hand, we monitor the response to capacitive human touch over a range of frequencies.Objects excited by an electrical signal responddifferently at different frequencies, therefore, the changes inthe return signal will also be frequency dependent. Thus, instead of measuring a single data point for each touchevent, we measure a multitude of data points at differentfrequencies.We then use machine learning and classification techniques to demonstrate that we can reliably extractrich interaction context, such as hand or body postures, fromthis data. Not only can we determine that a touch event occurred,we can also determine how it occurred. Importantly,this contextual touch information is captured through a singleelectrode, which could be simply the object itself


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