Evidence map›Paper›PMID 40062240›Full record

ArticlePeerJ. Computer science2025

Research on sports activity behavior prediction based on electromyography signal collection and intelligent sensing channel.

Fengjin Ye, Yuchao Zhao, Zohaib Latif

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ. Computer science, 2025. 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
–field-weighted citation impact
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.

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

3 authors.

Fengjin YeCollege of Ocean Culture and Tourism, Xiamen Ocean Vocational College, Xiamen, Fujian, China.
Yuchao ZhaoCollege of Information Engineering, Xiamen Ocean Vocational College, Xiamen, China.
Zohaib LatifDepartment of Computer Science, School of Engineering and Digital Sciences (SEDS), Nazarbayev University, Astana, Kazakhstan.ORCID 0000-0002-5373-8063

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sports behavior prediction requires precise and reliable analysis of muscle activity during exercise. This study proposes a multi-channel correlation feature extraction method for electromyographic (EMG) signals to overcome challenges in sports behavior prediction. A wavelet threshold denoising algorithm is enhanced with nonlinear function transitions and control coefficients to improve signal quality, achieving effective noise reduction and a higher signal-to-noise ratio. Furthermore, multi-channel linear and nonlinear correlation features are combined, leveraging mutual information estimation

Indexed as

Copula entropyElectromyographic signalsMulti channel correlation featuresMutual informationWavelet thresholding

Identifiers

PMID40062240
PMCPMC11888918

What OpenQuestion holds

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LicenceCC BY
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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.