PERSPECTIVE article
Front. Sports Act. Living
Sec. Biomechanics and Control of Human Movement
Volume 7 - 2025 | doi: 10.3389/fspor.2025.1634656
This article is part of the Research TopicAdvancing Biomechanics: Enhancing Sports Performance, Mitigating Injury Risks, and Optimizing Athlete Rehabilitation - Volume IIView all 7 articles
Neuromechanical Adaptations to EMG-Guided SSC Training in Elite Badminton Players: A Predictive Multivariate Approach
Provisionally accepted
- 1Gdansk University of Physical Education and Sport, Gdańsk, Poland
- 2Akademia Nauk Stosowanych w Walczu, Walcz, Poland
- 3Akademia Wychowania Fizycznego i Sportu im Jedrzeja Sniadeckiego w Gdansku, Gdask, Poland
- 4Akademia Wychowania Fizycznego imienia Jerzego Kukuczki w Katowicach, Katowice, Poland
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The stretch-shortening cycle (SSC) is essential for explosive lower-limb actions in court-based sports like badminton. Traditional jump assessments may miss subtle neuromechanical changes. Recent developments in real-time electromyography (EMG) and multivariate analysis-such as synergy-based models-enable more precise, individualized diagnostics in sport-specific contexts.This study examined the neuromechanical effects of a four-week EMG-guided SSC training program in elite badminton players and developed predictive models to identify early training responders.Twenty-four national-level athletes were randomized into an experimental group (EG, n = 12), receiving EMG-guided feedback, and a control group (CG, n = 12), performing similar tasks with sham feedback. Key outcome measures included reactive strength index (RSI), impulse metrics, and EMG latency, recorded pre-and post-intervention. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were used to assess adaptations. Random Forest and Multilayer Perceptron (MLP) models predicted post-intervention responder status.The EG demonstrated significant improvements in EMG latency (-12.2 to -16.5 ms, p < 0.05), RSI (+13.4%, p = 0.014), and impulse dynamics. PCA identified five components explaining 78.3% of the total variance, with EG athletes clustering around neuromuscular timing dimensions. LDA showed moderate group separation (AUC = 0.72). ML models performed well in classification (AUC = 0.92; F1 = 0.89), though small sample size raises concerns of overfitting.EMG-guided SSC training promotes meaningful neuromechanical adaptation in elite players.Machine learning and dimensionality reduction may help detect early performance shifts, though findings require validation in larger, more diverse cohorts.
Keywords: biofeedback, latency, impulse, adaptation, classification MeSH-aligned: Electromyography, Motor Activity, Physical Conditioning, Human, Time Factors
Received: 24 May 2025; Accepted: 21 Jul 2025.
Copyright: © 2025 Prończuk, Skalski, Łosińska, Maszczyk and Gołaś. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Kinga Łosińska, Gdansk University of Physical Education and Sport, Gdańsk, Poland
Adam Maszczyk, Gdansk University of Physical Education and Sport, Gdańsk, Poland
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