  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.
