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 36, Number 11(2) (2024)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 4757-4774
S&M3829 Research Paper of Special Issue https://doi.org/10.18494/SAM5181 Published: November 19, 2024 Contactless Heart Rate and Heart Rate Variability Estimation Using Palm Images with Particle Swarm Optimization and Independent Component Analysis Algorithms [PDF] Te-Jen Su, Wei-Hong Lin, Wen-Rong Yang, Ya-Chung Hung, Qian-Yi Zhuang, Shih-Ming Wang, and Li-chin Tseng (Received June 10, 2024; Accepted October 11, 2024) Keywords: particle swarm optimization algorithm, heart rate, heart rate variability, independent component analysis, contactless measurement
Since the coronavirus disease 2019 (COVID-19) pandemic, some patients with COVID-19 have experienced abnormal heart rates, posing potential health risks. In this study, we develop a noncontact method for measuring heart rate (HR) and heart rate variability (HRV) to effectively reduce the risk of infection and assist healthcare professionals in achieving accurate diagnosis and treatment. In this research, we collected data from 20 experimental testers based on palm images captured from photoplethysmography signals and measuring HR and HRV data by combining intelligent algorithms, namely, separation methods by particle swarm optimization and independent component analysis signal. The proposed method’s new contactless measurement performance can effectively eliminate infection concerns and obtain HR and HRV rapidly and handily. Moreover we provide higher accuracies for physiological parameters, namely, root mean square error (2.00 bpm), mean absolute percentage error (1.5%), and measurement time (8 s), than those in recently published literature.
Corresponding author: Shih-Ming Wang  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Te-Jen Su, Wei-Hong Lin, Wen-Rong Yang, Ya-Chung Hung, Qian-Yi Zhuang, Shih-Ming Wang, and Li-chin Tseng, Contactless Heart Rate and Heart Rate Variability Estimation Using Palm Images with Particle Swarm Optimization and Independent Component Analysis Algorithms, Sens. Mater., Vol. 36, No. 11, 2024, p. 4757-4774. |
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- Deep Learning Prediction Model of Mortality Including Brain-type Natriuretic Peptide in Patients with Acute Decompensated Heart Failure
Hirotaka Takizawa, Masatoshi Minamisawa, Hirohiko Motoki, Koichiro Kuwahara, and Masaki Yamaguchi
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