Learning to recognize touch gestures: recurrent vs. convolutional features and dynamic sampling

TL;DR

This paper introduces a deep learning approach using convolutional neural networks with dynamic sampling for recognizing touch gestures, achieving high accuracy and robustness across different users and devices.

Contribution

It presents a novel dynamic sampling and normalization technique for gesture representation and demonstrates superior performance on new and existing datasets.

Findings

Achieved near-perfect accuracy on the MMG dataset.

Introduced a new multi-touch gesture dataset with 6591 gestures.

Outperformed state-of-the-art methods on standard benchmarks.

Abstract

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.