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Authors: Andualem Maereg 1 ; Yang Lou 2 ; Emanuele Secco 1 and Raymond King 2

Affiliations: 1 Robotics Lab, Liverpool Hope University, Liverpool, U.K. ; 2 Facebook Reality Labs, Redmond, WA, U.S.A.

ISBN: 978-989-758-402-2

ISSN: 2184-4321

Keyword(s): Hand Gesture, NIR, Human-machine Interaction (HCI), Bio-sensing, Virtual-reality, Wearable Sensing.

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. (More)

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Paper citation in several formats:
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 - Volume 2: HUCAPP, ISBN 978-989-758-402-2, ISSN 2184-4321, pages 110-117. DOI: 10.5220/0008909401100117

@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 - Volume 2: HUCAPP,},
year={2020},
pages={110-117},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008909401100117},
isbn={978-989-758-402-2},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - 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

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