Next Article in Journal
A Low-Delay Lightweight Recurrent Neural Network (LLRNN) for Rotating Machinery Fault Diagnosis
Previous Article in Journal
Empowering the Internet of Vehicles with Multi-RAT 5G Network Slicing
Previous Article in Special Issue
Quantifying Caregiver Movement when Measuring Infant Movement across a Full Day: A Case Report
Article Menu

Export Article

Open AccessArticle

An EMG Patch for the Real-Time Monitoring of Muscle-Fatigue Conditions During Exercise

1
Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung City 41349, Taiwan
2
Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung City 41349, Taiwan
3
Biomedical Information Engineering Laboratory, University of Aizu, Aizu-wakamatsu City, Fukushima 965-8580, Japan
4
Department of Industrial Design, Chaoyang University of Technology, Taichung City 41349, Taiwan
5
Department of Leisure Services Management, Chaoyang University of Technology, Taichung City 41349, Taiwan
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(14), 3108; https://doi.org/10.3390/s19143108
Received: 21 May 2019 / Revised: 7 July 2019 / Accepted: 12 July 2019 / Published: 14 July 2019
(This article belongs to the Special Issue Wearable Wireless Sensors)
  |  
PDF [5166 KB, uploaded 14 July 2019]
  |     |  

Abstract

In recent years, wearable monitoring devices have been very popular in the health care field and are being used to avoid sport injuries during exercise. They are usually worn on the wrist, the same as sport watches, or on the chest, like an electrocardiogram patch. Common functions of these wearable devices are that they use real time to display the state of health of the body, and they are all small sized. The electromyogram (EMG) signal is usually used to show muscle activity. Thus, the EMG signal could be used to determine the muscle-fatigue conditions. In this study, the goal is to develop an EMG patch which could be worn on the lower leg, the gastrocnemius muscle, to detect real-time muscle fatigue while exercising. A micro controller unit (MCU) in the EMG patch is part of an ARM Cortex-M4 processor, which is used to measure the median frequency (MF) of an EMG signal in real time. When the muscle starts showing tiredness, the median frequency will shift to a low frequency. In order to delete the noise of the isotonic EMG signal, the EMG patch has to run the empirical mode decomposition algorithm. A two-electrode circuit was designed to measure the EMG signal. The maximum power consumption of the EMG patch was about 39.5 mAh. In order to verify that the real-time MF values measured by the EMG patch were close to the off-line MF values measured by the computer system, we used the root-mean-square value to estimate the difference in the real-time MF values and the off-line MF values. There were 20 participants that rode an exercise bicycle at different speeds. Their EMG signals were recorded with an EMG patch and a physiological measurement system at the same time. Every participant rode the exercise bicycle twice. The averaged root-mean-square values were 2.86 ± 0.86 Hz and 2.56 ± 0.47 Hz for the first and second time, respectively. Moreover, we also developed an application program implemented on a smart phone to display the participants’ muscle-fatigue conditions and information while exercising. Therefore, the EMG patch designed in this study could monitor the muscle-fatigue conditions to avoid sport injuries while exercising. View Full-Text
Keywords: electromyogram; patch; muscle fatigue; application program electromyogram; patch; muscle fatigue; application program
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Liu, S.-H.; Lin, C.-B.; Chen, Y.; Chen, W.; Huang, T.-S.; Hsu, C.-Y. An EMG Patch for the Real-Time Monitoring of Muscle-Fatigue Conditions During Exercise. Sensors 2019, 19, 3108.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top