Young Researcher Paper Award 2023
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Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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Sensors and Materials
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 35, Number 12(1) (2023)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 4117-4129
S&M3471 Research Paper of Special Issue https://doi.org/10.18494/SAM4592 Published: December 15, 2023 Real-time Hand Movement Trajectory Tracking with Deep Learning [PDF] Po-Tong Wang, Jia-Shing Sheu, and Chih-Fang Shen (Received July 15, 2023; Accepted November 9, 2023) Keywords: real-time hand tracking, deep learning, single-shot multibox detector (SSD), CAMShift, object detection, human–computer interaction (HCI)
In this study, we employed deep learning to develop a real-time hand trajectory tracking system. Our primary approach integrates the MobileNetv2 single-shot multibox detector, known for accuracy, with the versatile CAMShift algorithm. This synergy ensures robust hand detection across diverse scenarios. Through rigorous testing on webcam images and leveraging advanced feature extraction methods, such as contour discernment and skin hue differentiation, we report an 88.17% increase in detection accuracy over traditional models. Moreover, with a latency of merely 0.0343 s, our system demonstrates its prowess in immersive gaming and assistive devices for individuals with disabilities
Corresponding author: Po-Tong Wang  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Po-Tong Wang, Jia-Shing Sheu, and Chih-Fang Shen, Real-time Hand Movement Trajectory Tracking with Deep Learning, Sens. Mater., Vol. 35, No. 12, 2023, p. 4117-4129. |
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