Muscle synergy analysis during badminton forehand overhead smash
Integrating Electromyography and Musculoskeletal Modeling
Bibliographic Data
| ID | 5285629 |
|---|---|
| Authors | Raheleh Tajik (Kharazmi University), Wissem Dhahbi (0000-0001-6221-546X, University of Jendouba), Hamed Fadaei (Kharazmi University), Raghad Mimar (0000-0002-0558-6421, Kharazmi University) |
| Year | 2025 |
| Volume | 7 |
| Pages | 1596670-1596670 |
| Publication date | 2025-06-03 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Sports and Active Living (JOURNAL) |
| Journal identifiers | ISSN: 2624-9367 • E-ISSN: 2624-9367 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fspor.2025.1596670 |
| PMID | 40529409 |
| OpenAlex | W4411006638 |
| Language | EN |
| Citations received | 2 |
| References cited | 52 |
IntroductionThis study aimed to quantify shoulder muscle synergies during badminton forehand overhead smash (BFOS) via non-negative matrix factorization (NMF), validate musculoskeletal (MSK) models for high-speed movements by comparing electromyography (EMG)-derived synergies with simulation results, and explore the potential of NMF-based MSK models in advancing sports science.MethodsTwenty elite badminton players (age: 24 ± 4 years; experience: 15 ± 4 years) performed maximal-effort BFOS while EMG signals from fifteen shoulder muscles were recorded. Three-dimensional motion analysis with a ten-camera Vicon system captured kinematic data at 100 Hz. A validated OpenSim upper extremity model was implemented to simulate muscle activations via static optimization. NMF extracted synergy vectors and activation coefficients from both experimental EMG and MSK modeling data.ResultsThree muscle synergies accounted for >90% variance in both analyses with no significant differences in global VAF (p = 0.12). The first synergy (trapezius-dominant) showed 95% EMG and 97% MSK variance; the second synergy (pectoralis/anterior deltoid) exhibited 97% EMG and 94% MSK variance; the third synergy (posterior muscles) demonstrated 95% EMG and 98% MSK variance. Strong agreement between approaches was observed for both weight vectors (W1:0.81 ± 0.04, W2:0.87 ± 0.01, W3:0.88 ± 0.03) and activation coefficients (C1:0.95 ± 0.02, C2:0.98 ± 0.01, C3:0.98 ± 0.01), with differences primarily in lower trapezius activation (similarity: 0.77 ± 0.05), likely due to challenges in recording deep muscle activity through surface electromyography. These findings validate the combined experimental-computational approach for analyzing complex, high-velocity movements.ConclusionThe strong correspondence between experimental and computational synergies validates MSK modeling for analyzing neuromuscular control during high-velocity overhead movements. The identified synergies provide a framework for understanding muscle coordination during BFOS, with potential applications in targeted training program optimization and injury prevention strategies in overhead sports
Electromyography · Overhead (engineering · Physical medicine and rehabilitation · Computer Science · Medicine · Motor Control and Adaptation · Muscle activation and electromyography studies · Sports Performance and Training
| Unique citing works | 2 |
|---|---|
| Citations per year | 2 |
| Citation span | 2025 - 2026 (2) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 2 |