WaveGlove: Transformer-based hand gesture recognition using multiple inertial sensors
This work addresses gesture recognition for human-computer interaction, but it is incremental as it builds on existing methods with a new hardware setup.
The paper tackled hand gesture recognition using multiple inertial sensors on a glove, showing that complex gestures can be recognized with high accuracy, with performance improving up to three sensors.
Hand Gesture Recognition (HGR) based on inertial data has grown considerably in recent years, with the state-of-the-art approaches utilizing a single handheld sensor and a vocabulary comprised of simple gestures. In this work we explore the benefits of using multiple inertial sensors. Using WaveGlove, a custom hardware prototype in the form of a glove with five inertial sensors, we acquire two datasets consisting of over $11000$ samples. To make them comparable with prior work, they are normalized along with $9$ other publicly available datasets, and subsequently used to evaluate a range of Machine Learning approaches for gesture recognition, including a newly proposed Transformer-based architecture. Our results show that even complex gestures involving different fingers can be recognized with high accuracy. An ablation study performed on the acquired datasets demonstrates the importance of multiple sensors, with an increase in performance when using up to three sensors and no significant improvements beyond that.