2021/05/04 by Matej Králik, Králik, Matej, Marek Šuppa +1
Computer Science · Engineering · Psychology · #FOS: Computer and information sciences #FOS: Electrical engineering #Hand Gesture Recognition Systems #Hearing Impairment and Communication #Human-Computer Interaction (cs.HC) #Indoor and Outdoor Localization Technologies #Machine Learning (cs.LG) #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2105.01753
openalex publication_date 2021/05/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.