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Background <p>While 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.</p> Methods <p>Twenty-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<sup>2</sup> = 0.85). Three-dimensional motion capture (200Hz) quantified hip-ankle and pelvis-torso coordination using vector coding techniques, while surface electromyography (EMG) of 12 lower limb muscles was analyzed via non-negative matrix factorization (NMF) to extract muscle synergy patterns. The NMF approach decomposes multi-muscle activation patterns into fundamental synergistic components, providing insight into neuromuscular control strategies.</p> Results <p>Compared 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%, <i>p</i> = 0.048), and (3) promoted greater muscle synergy complexity (median synergies: HG = 7 vs LG = 6, <i>p</i> = 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, <i>p</i> = 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.</p> Conclusion <p>High-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.</p>

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Impact of Neural network-quantified musical groove on cyclists’ joint coordination and muscle synergy: a repeated measures study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月7日

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  • Haojie Li,
  • Xinyu Lin,
  • Xie Wu

摘要

Background

While 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.

Methods

Twenty-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 (R2 = 0.85). Three-dimensional motion capture (200Hz) quantified hip-ankle and pelvis-torso coordination using vector coding techniques, while surface electromyography (EMG) of 12 lower limb muscles was analyzed via non-negative matrix factorization (NMF) to extract muscle synergy patterns. The NMF approach decomposes multi-muscle activation patterns into fundamental synergistic components, providing insight into neuromuscular control strategies.

Results

Compared 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.

Conclusion

High-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.