FingerCloud

We describe a particle filtering approach to inferring finger movements on capacitive sensing arrays. This technique allows the efficient combination of human movement models with accurate sensing models, and gives high-fidelity results with low-resolution sensor grids and tracks finger height. Our model provides uncertainty estimates, which can be linked to the interaction to provide appropriately smoothed responses as sensing perfomance degrades; system autonomy is increased as estimates of user behaviour become less certain. We demonstrate the particle filter approach with a map browser running with a very small sensor board, where finger position uncertainty is linked to autonomy handover.