ArticleJournal of neuroengineering and rehabilitation2025
Impact of Neural network-quantified musical groove on cyclists' joint coordination and muscle synergy: a repeated measures study.
Article in Journal of neuroengineering and rehabilitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Effects of NMES Combined with Resistance Training Using Underwater Surface EMG Sensors on Neuromuscular Activation of Breaststroke Technique in Breaststroke Athletes: Analysis of Non-Negative Matrix Muscle Synergy.Sensors (Basel, Switzerland) · 2026Trial
- Muscle Synergy Analysis of Different PAPE Protocols on Side Kick Performance in Elite Sanda Athletes: A Repeated Measures Study.Sensors (Basel, Switzerland) · 2026Trial
- Effects of NMES Combined with Water-Based Resistance Training on Muscle Coordination in Freestyle Kick Movement.Sensors (Basel, Switzerland) · 2026Article
- Differences in Muscle Inter-Phasic Coherence During Side Kick Techniques Among Male Sanda Athletes of Different Skill Levels Based on Sensor Analysis: A Cross-Sectional Study.Sensors (Basel, Switzerland) · 2026Article
- Sensor-based assessment of the acute effects of NMES combined with weighted squats on muscle activation and lower limb joint coordination during the stop-and-cut task.Frontiers in physiology · 2026Article
- Muscle synergy and performance characteristics during regular jump shooting in basketball players of different skill levels: a non-negative matrix factorization analysis.Frontiers in physiology · 2026Article
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3 authors.
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Abstract
backgroundWhile music is known to influence exercise performance through auditory-motor coupling, the biomechanical mechanisms by which musical groove-characterized by rhythmic drive and movement-inducing qualities-modulates cycling coordination remain poorly understood. This study investigates how groove levels systematically alter lower extremity kinematics and neuromuscular control during high-torque cycling.
methodsTwenty-four well-trained, right-handed cyclists completed high-torque cycling trials under three counterbalanced conditions: metronome (control), low-groove (LG), and high-groove (HG) music, with groove levels objectively classified by a validated deep learning model (R
resultsCompared to LG and control conditions, HG music significantly: (1) increased hip-ankle in-phase coordination by 28.7% (HG:29.8% vs. LG:23.2%, p = 0.020), (2) enhanced pelvis-torso synchronization by 27.1% (HG:38.0% vs. LG:29.9%, p = 0.048), and (3) promoted greater muscle synergy complexity (median synergies: HG = 7 vs LG = 6, p = 0.039). Notably, the soleus (SOL) muscle-crucial for ankle stabilization-showed significantly higher activation weights in HG condition (0.11 ± 0.03 vs 0.04 ± 0.02, p = 0.030), suggesting improved distal control. The emergence of a unique erector spinae-gastrocnemius lateralis (ES-GL) synergy pattern (present in 54% of HG trials) indicates enhanced trunk-limb coupling under high-groove conditions.
conclusionHigh-groove music promotes more coordinated movement patterns during cycling through two key mechanisms: (1) optimized joint coordination, particularly in proximal-distal (hip-ankle) and axial (pelvis-torso) linkages, and (2) reorganization of neuromuscular control strategies evidenced by increased synergy complexity and selective activation of postural stabilizers (SOL). These findings provide biomechanical evidence supporting groove-based auditory-motor interventions, though direct performance benefits require verification through additional kinetic and metabolic measures. The successful application of deep learning for groove quantification establishes a framework for personalized music selection in sports and rehabilitation contexts.
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