AnglePose: robust, precise capacitive touch tracking via 3d orientation estimation

Abstract

We present a finger-tracking system for touch-based interaction which can track 3D finger angle in addition to position, using low-resolution conventional capacitive sensors, therefore compensating for the inaccuracy due to pose variation in conventional touch systems. Probabilistic inference about the pose of the finger is carried out in real-time using a particle filter; this results in an efficient and robust pose estimator which also gives appropriate uncertainty estimates. We show empirically that tracking the full pose of the finger results in greater accuracy in pointing tasks with small targets than competitive techniques. Our model can detect and cope with different finger sizes and the use of either fingers or thumbs, bringing a significant potential for improvement in one-handed interaction with touch devices. In addition to the gain in accuracy we also give examples of how this technique could open up the space of novel interactions.