摘要
目的:探究运动经验对起跑时肌肉协同特征的影响,分析不同运动经验下肌肉协同与姿态稳定性之间的关系。方法:采用三维动作捕捉系统、三维测力台和无线表面肌电采集设备同步采集11名高水平短跑运动员和14名短跑初学者在起跑时的运动学、地面反作用力和表面肌电数据。将2组受试者的表面肌电包络线映射至脊髓各节段的α−运动神经元池以获取脊髓水平的运动输出,采用非负矩阵分解和K-Means聚类获取2组受试者的肌肉协同特征。基于非受控流形分析建立各组肌肉协同变异性与质心横向位移波动之间的关系。结果:1)高水平组L1~L3节段的输出在起跑时与低水平存在显著差异(P<0.05),高水平组L4~S3节段的输出在起跑末期显著高于低水平组(P<0.05);2)高水平组的各组肌肉协同的空间结构较为合理,激活时间更为集中(P<0.05);3)高水平组在起跑时各组协同之间的协调程度较高,低水平组的各组肌肉协同仅在预备姿态时具有较好的协调。结论:运动经验可以优化各组肌肉协同间的协调及各协同内部的时空结构。合理调整预备姿态的肌肉激活策略并提高起跑器蹬离技术,有助于促进起跑的运动表现。
Objective:To investigate the effect of exercise experience on muscle synergy characteristics during the start and to analyze the relationship between muscle synergy and postural stability under different exercise experiences.Methods:Kinematic,ground reaction force and surface electromyography(sEMG)data of 11 high-level sprinters and 14 beginner sprinters at the start were synchronously collected using a 3D motion capture system,a 3D force plate and a wireless sEMG acquisition system.The sEMG envelopes of the two groups of subjects were mapped to theα-motor neuron pools in each segment of the spinal cord to obtain the motor output at the spinal cord level,and the muscle synergy characteristics of the two groups of subjects were obtained by using non-negative matrix factorization(NMF)and K-Means clustering.The relationship between muscle synergy variability and fluctuations in lateral displacement of mass center in each group was established based on uncontrolled manifold(UCM)analysis.Results:1)The output of L1 to L3 segments in the high-level group was significantly different from that of the low-level at the start(P<0.05),and the output of L4 to S3 segments in the high-level group was significantly higher than that of the low-level group at the end of the start(P<0.05);2)the spatial structure of the muscle synergies of each group was more reasonable and the activation time was more concentrated in the high-level group(P<0.05);3)the high-level group had a higher degree of coordination among the synergies at the start,while the low-level group had a better coordination among the muscle synergies only in the preparatory stance.Conclusions:Exercise experience optimized coordination between muscle synergies and the spatio-temporal structure within each synergy.Rationalization of muscle activation strategies in the preparatory stance and improvement of the starter’s starting technique can contribute to the athletic performance at the start.
作者
潘正晔
刘陆帅
孙媛
苏荣海
马运超
PAN Zhengye;LIU Lushuai;SUN Yuan;SU Ronghai;MA Yunchao(Beijing Normal University,Beijing 100091,China)
出处
《中国体育科技》
CSSCI
北大核心
2024年第2期3-10,共8页
China Sport Science and Technology
基金
教育部人文社会科学研究青年项目(19YJC890030)
北京市社会科学基金一般项目(22YTB009)。
关键词
短跑起跑
运动控制
肌肉协同
非受控流形分析
机器学习
sprint start
motor control
muscle synergy
uncontrolled manifold analysis
machine learning
