Frontiers in sports and active living · 2025
Neuromechanical adaptations to EMG-guided SSC training in elite badminton players: a predictive multivariate approach.
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Prończuk Magdalena, Skalski Dariusz, Łosińska Kinga, Maszczyk Adam, Gołaś Artur
Аннотация
BACKGROUND: 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.
OBJECTIVES: This study examined the neuromechanical effects of a 4-week EMG-guided SSC training program in elite badminton players and developed predictive models to identify early training responders.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
OBJECTIVES: This study examined the neuromechanical effects of a 4-week EMG-guided SSC training program in elite badminton players and developed predictive models to identify early training responders.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.