Evidence map›Paper›PMID 40295627›Full record

ArticleScientific reports2025

Identification and quantification of muscular cocontraction for ankle rehabilitation through variational mode decomposition in surface electromyography.

Sania Yasmeen, Asim Waris, Faisal Amin, Javaid Iqbal, Syed Omer Gilani, Muhammad Jawad Khan, Fawwaz Hazzazi, Ahmed Imran, Umer Hameed Shah, Muhammad Adeel Ijaz

Abstract read
In one paragraph

Article in Scientific reports, 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

10 authors.

Sania YasmeenDepartment of Biomedical Engineering and Sciences, School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan.
Asim WarisDepartment of Biomedical Engineering and Sciences, School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan.
Faisal AminDepartment of Biomedical Engineering and Sciences, School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan.
Javaid IqbalDepartment of Biomedical Engineering and Sciences, School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan.
Syed Omer GilaniDepartment of Electrical, Computer, and Biomedical Engineering, Abu Dhabi University, Abu Dhabi, UAE.
Muhammad Jawad KhanDepartment of Electrical Engineering, College of Engineering, Prince Sattam bin Abdulaziz University, 11942, Al-Kharj, Saudi Arabia. mt.khan@psau.edu.sa.
Fawwaz HazzaziDepartment of Electrical Engineering, College of Engineering, Prince Sattam bin Abdulaziz University, 11942, Al-Kharj, Saudi Arabia.
Ahmed ImranDepartment of Biomedical Engineering & Artificial Intelligence Research Center, College of Engineering and Information Technology, Ajman University, Ajman, UAE.
Umer Hameed ShahDepartment of Biomedical Engineering & Artificial Intelligence Research Center, College of Engineering and Information Technology, Ajman University, Ajman, UAE.
Muhammad Adeel IjazDepartment of Biomedical Engineering and Sciences, School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ankle joint plays important role in performing fundamental activities such as walking and other essential daily tasks. Ankle stabilization and muscle co-contraction are crucial for rehabilitating gait abnormalities, as impaired ankle function disrupts gait, causes pain and inflammation, and hampers recovery. Accurate assessment of muscle co-contraction is crucial for developing effective non-pharmacological interventions. This paper introduces a novel approach using Variational Mode Decomposition (VMD) combined with scalogram visualization technique to analyze surface electromyographic (sEMG) signals from antagonist muscles of the lower limb and assesses muscular co-contraction using the coscalogram function. The present study compares VMD with the Continuous Wavelet Transform (CWT) approach and shows that VMD outperforms CWT in terms of both SNR and RMSE. On average, the increase in SNR in case of VMD (-17.65 ± 8.1dB to 2.98 ± 2.2dB) was greater than that of CWT (-17.65 ± 3.7dB to 1.34 ± 1.5dB). Similarly, the reduction in RMSE with VMD (0.023 ± 0.0029 to 0.017 ± 0.0015) surpassed that achieved with CWT (0.023 ± 0.0027 to 0.020 ± 0.0025). This enhanced accuracy in identifying co-contraction events has the potential to significantly improve clinical assessment and rehabilitation strategies for patients with ankle joint pathologies. To further validate VMD's effectiveness, we quantitatively assessed co-contraction events by comparing mean peak amplitudes identified using VMD and CWT. Our analysis, which revealed that VMD consistently captured stronger co-contraction events (higher mean peak amplitudes), supports VMD's superiority in accurately identifying and quantifying ankle muscle co-contraction. These results have significant implications for clinical practice, offering the potential for more precise assessments of ankle joint function and the development of more targeted and effective rehabilitation interventions.

Indexed as

AnkleAnkle JointElectromyographyMuscle ContractionMuscle, SkeletalAdultFemaleHumansMaleWavelet AnalysisYoung AdultAnkle rehabilitationCo-contractionContinuous wavelet transformScalogramsEMGVariational mode decomposition

Identifiers

PMID40295627
PMCPMC12037877

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.