Evidence map›Paper›PMID 31337107›Full record

ArticleSensors (Basel, Switzerland)2019

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

Shing-Hong Liu, Chuan-Bi Lin, Ying Chen, Wenxi Chen, Tai-Shen Huang, Chi-Yueh Hsu

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed
4.1field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

35 citing papers in PubMed, 99 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 2 countries.

Shing-Hong LiuDepartment of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung City 41349, Taiwan.ORCID 0000-0002-3923-4387
Chuan-Bi LinDepartment of Information and Communication Engineering, Chaoyang University of Technology, Taichung City 41349, Taiwan. cblin@cyut.edu.tw.ORCID 0000-0001-9090-6583
Ying ChenBiomedical Information Engineering Laboratory, University of Aizu, Aizu-wakamatsu City, Fukushima 965-8580, Japan.
Wenxi ChenBiomedical Information Engineering Laboratory, University of Aizu, Aizu-wakamatsu City, Fukushima 965-8580, Japan.
Tai-Shen HuangDepartment of Industrial Design, Chaoyang University of Technology, Taichung City 41349, Taiwan.
Chi-Yueh HsuDepartment of Leisure Services Management, Chaoyang University of Technology, Taichung City 41349, Taiwan.
Chaoyang University of Technology · TWUniversity of Aizu · JP

Funding

Ministry of Science and Technology, Taiwan MOST107-2221-E-324-001
6 · The paper itself

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.

Indexed as

Signal Processing, Computer-AssistedAdultAlgorithmsBicyclingElectromyographyEquipment DesignExerciseFemaleHealthy VolunteersHumansMaleMonitoring, PhysiologicMuscle ContractionMuscle FatigueWearable Electronic DevicesYoung Adultapplication programelectromyogrammuscle fatiguepatch

Identifiers

PMID31337107
PMCPMC6679275
OpenAlexW2957348539

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.