Evidence map›Paper›PMID 36483292›Full record

ArticleMethodsX2022

Analysis of bilateral muscle coordination for characterizing neuromuscular function in postural control.

Arunee Promsri

Open access · goldAbstract read
In one paragraph

Article in MethodsX, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 56% 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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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

1 author at 1 institution in 2 countries.

Arunee PromsriDepartment of Physical Therapy, School of Allied Health Sciences, University of Phayao, Phayao, 56000, Thailand.
Universität Innsbruck · AT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coordination between legs is necessary to complete bipedal balance task goals. Assessing bilateral homonymous muscle coordination may provide insight into the inherent neuromuscular control of the two legs in achieving equilibrium. This work introduced a method based on a normalized cross-correlation analysis to analyze bilateral homonymous muscle coordination during bipedal balancing on different support surfaces, revealing the temporal similarity in shape (i.e., form) between two electromyographic (EMG) signals (i.e., EMG-EMG correlation). Two levels of EMG-EMG correlation were considered: individual homonymous muscles and groups (patterns) of homonymous muscles relevant to the current task. In order to analyze the patterns of homonymous muscles, a principal component analysis (PCA) was applied to the cross-correlation coefficients to provide insights into functionally specialized groups of homonymous muscles constrained by the nervous system to work cooperatively. This proposed method has advantages that can be applied to several purposes. For example,•Analyzing the EMG-EMG correlation provides essential information about the inherent neuromuscular function in postural control.•At the level of individual homonymous muscles, this method can be applied to assess the neuromuscular performance after injury to the specific muscles.•At the level of multiple homonymous muscles, this method can be used to monitor the cooperative work of several pairs of homonymous muscles in achieving equilibrium.

Indexed as

BalanceCOP, Center of PressureCross-correlation analysisElectromyography (EMG)EMG, ElectromyographyHomonymous muscleNeuromuscular controlPCA, Principal Component AnalysisPrincipal component analysis (PCA)StabilityUnstable surface

Identifiers

PMID36483292
PMCPMC9723914
OpenAlexW4310262421

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.