RidgePad

with Patrick Baudisch

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RidgePad

RidgePad derives finger posture and user ID from each touch event and thereby obtains 1.8 times higher accuracy than capacitive sensing. RidgePad is based on an L SCAN Guardian fingerprint scanner.

Description

RidgePad is a high-precision touch-input device that is based on the Generalized Perceived Input Point Model. It models touch on per-posture and per-user basis, reduces the fat finger problem, and thereby increases input accuracy. RidgePad extracts posture and user ID from the user's fingerprint during each touch interaction. In a user study, it achieved 1.8 times higher accuracy than a simulated capacitive baseline condition. The increase in accuracy can be used to make touch interfaces more reliable.

project page at Hasso Plattner Institute/HCI group