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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 33, Number 1(3) (2021)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 485-497
S&M2470 Research Paper of Special Issue https://doi.org/10.18494/SAM.2021.3034 Published: January 31, 2021 Smart Driver Drowsiness Detection Model Based on Analytic Hierarchy Process [PDF] Ting-Cheng Chang, Min-Hao Wu, Phan-Zhu Kim, and Ming-Hui Yu (Received July 20, 2020; Accepted November 16, 2020) Keywords: drowsiness detection, electrocardiogram, ECG, heart rate, new features
This paper proposes a smart driver drowsiness detection (SDDD) model for vehicles. The SDDD monitors a driver’s heart rate variability (HRV) through electrocardiography (ECG) in real time to detect driver drowsiness. The SDDD processes the data of HRV and ECG to obtain a set of parameters with time-domain analysis, frequency-domain analysis, detrended fluctuation analysis, approximate entropy, and sample entropy. In the process, a machine learning algorithm analyzes the parameters to detect driver drowsiness. The SDDD optimizes critical features with the analytic hierarchy process (AHP), which uses a feature extraction method through an iterative procedure. It is found that the SDDD in this study detects the level of driver drowsiness with higher sensitivity than previous models.
Corresponding author: Min-Hao Wu  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Ting-Cheng Chang, Min-Hao Wu, Phan-Zhu Kim, and Ming-Hui Yu, Smart Driver Drowsiness Detection Model Based on Analytic Hierarchy Process, Sens. Mater., Vol. 33, No. 1, 2021, p. 485-497. |
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