{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T01:45:10Z","timestamp":1793238310953,"version":"4.5.0"},"reference-count":65,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T00:00:00Z","timestamp":1757548800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Sports Act. Living"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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\u2014such as synergy-based models\u2014enable more precise, individualized diagnostics in sport-specific contexts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Twenty-four national-level athletes were randomized into an experimental group (EG,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200912), receiving EMG-guided feedback, and a control group (CG,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200912), 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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The EG demonstrated significant improvements in EMG latency (\u221212.2 to \u221216.5\u2005ms,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&amp;lt;\u20090.05), RSI (+13.4%,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.014), 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\u2009=\u20090.72). ML models performed well in classification (AUC\u2009=\u20090.92;\n                      <jats:italic>F<\/jats:italic>\n                      1\u2009=\u20090.89), though small sample size raises concerns of overfitting.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fspor.2025.1634656","type":"journal-article","created":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T05:26:10Z","timestamp":1757568370000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Neuromechanical adaptations to EMG-guided SSC training in elite badminton players: a predictive multivariate 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