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Hand Gesture Recognition (HGR) based on inertial data has grown considerablyin recent years, with the state-of-the-art approaches utilizing a singlehandheld 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 fiveinertial 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 ofMachine Learning approaches for gesture recognition, including a newly proposedTransformer-based architecture. Our results show that even complex gesturesinvolving different fingers can be recognized with high accuracy.  An ablation study performed on the acquired datasets demonstrates theimportance of multiple sensors, with an increase in performance when using upto three sensors and no significant improvements beyond that.

WaveGlove: Transformer-based hand gesture recognition using multiple inertial sensors