Young Researcher Paper Award 2021
🥇Winners

Notice of retraction
Vol. 32, No. 8(2), S&M2292

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.
Sensors and Materials
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 34, Number 6(4) (2022)
Copyright(C) MYU K.K.
pp. 2403-2425
S&M2980 Research Paper of Special Issue
https://doi.org/10.18494/SAM3787
Published: June 30, 2022

Sarcopenia Recognition System Combined with Electromyography and Gait Obtained by the Multiple Sensor Module and Deep Learning Algorithm [PDF]

I-Miao Chen, Pin-Yu Yeh, Ting-Chi Chang, Ya-Chu Hsieh, and Chiun-Li Chin

(Received December 27, 2021; Accepted May 31, 2022)

Keywords: wearable sensors, MSM, EAG, Bodi algorithm, gait indicators, LCNet

At present, many diseases can be predicted through data obtained by wearable sensors. The majority of these proposed wearable devices only use inertial sensors to obtain the walking motion signals of a subject. However, since the symptoms of sarcopenia are reflected in the changes in human muscles, we propose a sarcopenia recognition system, which consists of hardware and software. The hardware is composed of multiple sensor module (MSM), which is a wearable device used to collect the signals of electromyography and gait (EAG). The software is composed of biomedical and inertial sensors algorithm (Bodi algorithm) and leg health classification net (LCNet). The Bodi algorithm is used to calculate various gait indicators after predicting the risk of sarcopenia obtained by LCNet. The accuracy of LCNet is 94.41%, its precision is 91.58%, its specificity is 95.81%, and its sensitivity is 91.58%. In the future, we expect to use the proposed MSM to collect additional subjects’ gait data and apply it to other disease predictions to assist physicians in disease diagnosis.

Corresponding author: Chiun-Li Chin


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
I-Miao Chen, Pin-Yu Yeh, Ting-Chi Chang, Ya-Chu Hsieh, and Chiun-Li Chin, Sarcopenia Recognition System Combined with Electromyography and Gait Obtained by the Multiple Sensor Module and Deep Learning Algorithm, Sens. Mater., Vol. 34, No. 6, 2022, p. 2403-2425.



Forthcoming Regular Issues


Forthcoming Special Issues

Special Issue on Biosensors and Biofuel Cells for Smart Community and Smart Life
Guest editor, Seiya Tsujimura (University of Tsukuba), Isao Shitanda (Tokyo University of Science), and Hiroaki Sakamoto (University of Fukui)


Special Issue on the International Multi-Conference on Engineering and Technology Innovation 2021 (IMETI2021)
Guest editor, Wen-Hsiang Hsieh (National Formosa University)
Conference website


Special Issue on Novel Sensors and Related Technologies on IoT Applications: Part 1-2
Guest editor, Teen-Hang Meen (National Formosa University), Wenbing Zhao (Cleveland State University), and Cheng-Fu Yang (National University of Kaohsiung)
Call for paper


Special Issue on Advanced Micro/Nanomaterials for Various Sensor Applications (Selected Papers from ICASI 2021)
Guest editor, Sheng-Joue Young (National United University), Shoou-Jinn Chang (National Cheng Kung University), Liang-Wen Ji (National Formosa University), and Yu-Jen Hsiao (Southern Taiwan University of Science and Technology)
Conference website
Call for paper


Special Issue on Advanced Technologies for Remote Sensing and Geospatial Analysis: Part 3
Guest editor, Dong Ha Lee (Kangwon National University) and Myeong Hun Jeong (Chosun University)
Call for paper


Special Issue on APCOT 2022
Guest editor, Yuelin Wang, Tie Li (Shanghai Institute of Microsystem and Information Technology) and Qingan Huang (Southeast University)
Conference website
Call for paper


Copyright(C) MYU K.K. All Rights Reserved.