Hand Gesture Recognition based on Near-infrared Sensing Wristband

Andualem T. Maereg, Yang Lou, Emanuele L. Secco, Raymond King

2020

Abstract

Wrist-worn gesture sensing systems can be used as a seamless interface for AR/VR interactions and control of various devices. In this paper, we present a low-cost gesture sensing system that utilizes near Infrared Emitters (600 - 1100 nm) and Photo-Receivers encompassing the wrist to infer hand gestures. The proposed system consists of a wristband comprising Infrared emitters and receivers, data acquisition hardware, data post-processing software, and gesture classification algorithms. During the data acquisition process, 24 near Infrared Emitters are sequentially switched on around the wrist, and twelve Photo-diodes measure the light reflected, refracted, and scattered by the tissues inside the wrist. The acquired data corresponding to different gestures are labeled and input into a machine learning algorithm for gesture classification. To demonstrated the accuracy and speed of the proposed system, real-time gesture sensing user studies were conducted. As a result of this comparison, we obtained an average accuracy of 98.06% with standard deviation of 1.82%. In addition, we evaluated that the system can perform six-eight gestures per second in real time using a desktop computer operating with Core i7-7800X CPU at 3.5GHz and 32 GB RAM.

Paper Citation

in Harvard Style

Maereg A., Lou Y., Secco E. and King R. (2020). Hand Gesture Recognition based on Near-infrared Sensing Wristband. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 2: HUCAPP; ISBN 978-989-758-402-2, SciTePress, pages 110-117. DOI: 10.5220/0008909401100117

in Bibtex Style

@conference{hucapp20,
author={Andualem T. Maereg and Yang Lou and Emanuele L. Secco and Raymond King},
title={Hand Gesture Recognition based on Near-infrared Sensing Wristband},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 2: HUCAPP},
year={2020},
pages={110-117},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008909401100117},
isbn={978-989-758-402-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 2: HUCAPP
TI - Hand Gesture Recognition based on Near-infrared Sensing Wristband
SN - 978-989-758-402-2
AU - Maereg A.
AU - Lou Y.
AU - Secco E.
AU - King R.
PY - 2020
SP - 110
EP - 117
DO - 10.5220/0008909401100117
PB - SciTePress