Skill-Level Dependent Lower Limb Muscle Synergy Patterns During Open-Stance Forehand Strokes in Competitive Tennis Players
Metadatos
Mostrar el registro completo del ítemAutor
Wang, Yucheng; Sun, Dong; Wang, Dongxu; Chen, Diwei; Li, Fengping; Zhou, Zhanyi; Cen, Xuanzhen; Song, Yang; Janicijevic, Danica; Gu, YaodongEditorial
MDPI
Materia
Tennis Forehand open stance Lower limb Muscle synergy Non-negative Matrix Factorization
Fecha
2025-04-27Referencia bibliográfica
Wang, Y.; Sun, D.; Wang, D.; Chen, D.; Li, F.; Zhou, Z.; Cen, X.; Song, Y.; Janicijevic, D.; Gu, Y. Skill-Level Dependent Lower Limb Muscle Synergy Patterns During Open-Stance Forehand Strokes in Competitive Tennis Players. Appl. Sci. 2025, 15, 4831. [DOI: 10.3390/app15094831]
Resumen
Background: The open-stance forehand is a fundamental technique in tennis, playing a crucial role in competitive performance. Its execution depends heavily on lower limb coordination and neuromuscular control. Athletes of different skill levels often display distinct muscle activation strategies. This study employs non-negative matrix factorization (NMF) to analyze lower limb muscle synergy patterns during the forehand open stance across skill levels and explores their potential influence on stroke performance. Methods: A total of 30 tennis players, including 15 elite and 15 amateur athletes, participated in this study. Surface electromyography (sEMG) was used to record the activity of major lower limb muscles during the forehand open stance. Muscle synergy patterns were extracted using NMF, and K-means clustering was applied to classify synergy patterns. Independent sample t-tests were conducted to examine differences between muscle synergies. Results: Significant differences (p < 0.05) were observed in the spatial characteristics of each synergy component across different movement phases. However, temporal characteristics showed a significant difference only in Syn2 during the mid-phase of the backswing (BS) (56.2–60.4%) (p = 0.033). Conclusions: Elite athletes exhibited more optimized and stable muscle activation patterns, enabling more efficient coordination of major muscle groups. Based on sEMG decomposition and muscle synergy analysis, these activation patterns may contribute to improved stroke efficiency and energy transfer and potentially reduce the risk of sports-related injuries.





