Neuromechanical adaptations to EMG-guided SSC training in elite badminton players: a predictive multivariate approach

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 4-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.