Evidence map›Paper›PMID 34156602›Full record

ArticleMedical & biological engineering & computing2021

Non-obstructive monitoring of muscle fatigue for low intensity dynamic exercise with infrared thermography technique.

Muhammad Faiz Md Shakhih, Nursyazana Ridzuan, Asnida Abdul Wahab, Nurul Farha Zainuddin, Laila Fadhillah Ulta Delestri, Anis Suzziani Rosslan, Mohammed Rafiq Abdul Kadir

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In one paragraph

Article in Medical & biological engineering & computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
2.2field-weighted citation impact, top 12% 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
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

7 authors at 2 institutions in 1 country.

Muhammad Faiz Md ShakhihSchool of Biomedical Engineering and Health Sciences, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia.
Nursyazana RidzuanSchool of Biomedical Engineering and Health Sciences, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia.
Asnida Abdul WahabSchool of Biomedical Engineering and Health Sciences, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia. asnida@biomedical.utm.my.ORCID http://orcid.org/0000-0003-3222-1732
Nurul Farha ZainuddinFaculty of Sports Science and Recreation, Universiti Teknologi MARA, Perlis Branch, Arau Campus, 02600, Arau, Perlis, Malaysia.
Laila Fadhillah Ulta DelestriSchool of Biomedical Engineering and Health Sciences, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia.
Anis Suzziani RosslanSchool of Biomedical Engineering and Health Sciences, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia.
Mohammed Rafiq Abdul KadirSchool of Biomedical Engineering and Health Sciences, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia.
University of Technology Malaysia · MYUniversiti Teknologi MARA · MY

Funding

Ministry of Higher Education 15J89Ministry of Higher Education, Malaysia 07G22
6 · The paper itself

Abstract

Surface electromyography (sEMG) has been widely used in evaluating muscle fatigue among athletes where electrodes are attached on the skin during the activity. Recently, infrared thermography technique (IRT) has gain popularity and shown to be another preferred method in monitoring and predicting muscle fatigue non-obstructively. This paper investigates the correlation between surface temperature and muscle activation parameters obtained using both IRT and sEMG methods simultaneously. Twenty healthy subjects were required to perform a repetitive calf raise exercise with various loads attached around their ankle for 3 min to induce fatigue on the targeted gastrocnemius muscles. Average temperature and temperature difference information were extracted from thermal images, while root mean square (RMS) and median frequency (MF) were extracted from sEMG signals. Spearman statistical analysis performed shows that there is a significant correlation between average temperature with RMS and between temperature difference with MF values at p<0.05. While ANOVA test conducted shows that there is significant impact of loads on RMS and MF where F=12.61 and 3.59, respectively, at p< 0.05. This study suggested that skin surface temperature can be utilized in monitoring and predicting muscle fatigue in low intensity dynamic exercise and can be extended to other dynamic exercises.

Indexed as

Muscle FatigueThermographyElectromyographyExerciseHumansMuscle, SkeletalInfrared thermographyMuscle fatiguePhysiological monitoring

Identifiers

PMID34156602
OpenAlexW3175292045

What OpenQuestion holds

Textmetadata
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.