  We propose a fully automatic method for learning gestures on big touch
devices in a potentially multi-user context. The goal is to learn general
models capable of adapting to different gestures, user styles and hardware
variations (e.g. device sizes, sampling frequencies and regularities).
  Based on deep neural networks, our method features a novel dynamic sampling
and temporal normalization component, transforming variable length gestures
into fixed length representations while preserving finger/surface contact
transitions, that is, the topology of the signal. This sequential
representation is then processed with a convolutional model capable, unlike
recurrent networks, of learning hierarchical representations with different
levels of abstraction.
  To demonstrate the interest of the proposed method, we introduce a new touch
gestures dataset with 6591 gestures performed by 27 people, which is, up to our
knowledge, the first of its kind: a publicly available multi-touch gesture
dataset for interaction.
  We also tested our method on a standard dataset of symbolic touch gesture
recognition, the MMG dataset, outperforming the state of the art and reporting
close to perfect performance.
