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

arXiv: https://arxiv.org/abs/2105.01753 · HTML: https://mlsanity.com/paper/2105.01753/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 4 May 2021.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities](https://mlsanity.com/paper/2001.07416/) (2020)
- [Human Activity Recognition from Wearable Sensor Data Using Self-Attention](https://mlsanity.com/paper/2003.09018/) (2020)
- [Human Activity Recognition using Inertial, Physiological and Environmental Sensors: a Comprehensive Survey](https://mlsanity.com/paper/2004.08821/) (2020)
- [Deep Learning-Based Gait Recognition Using Smartphones in the Wild](https://mlsanity.com/paper/1811.00338/) (2018)
- [Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-Art](https://mlsanity.com/paper/1806.05226/) (2018)
- [Deep Fisher Discriminant Learning for Mobile Hand Gesture Recognition](https://mlsanity.com/paper/1707.03692/) (2017)
- [Online Human Gesture Recognition using Recurrent Neural Networks and Wearable Sensors](https://mlsanity.com/paper/1810.11297/) (2018)
- [GestureKeeper: Gesture Recognition for Controlling Devices in IoT Environments](https://mlsanity.com/paper/1903.06643/) (2019)

## Built on by (most cited first, 1 of 1)

- [SignSpeak: Open-Source Time Series Classification for ASL Translation](https://mlsanity.com/paper/2407.12020/) (2024)

## Abstract (arXiv)

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.