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疲劳对跑步时选定下肢肌肉协同作用的影响。

The effects of fatigue on synergy of selected lower limb muscles during running.

机构信息

Faculty of Sport Scienses, Bu Ali University, Hamedan, Iran.

Faculty of Sport Scienses, Bu Ali Sina University, Hamedan, Iran.

出版信息

J Biomech. 2020 Apr 16;103:109692. doi: 10.1016/j.jbiomech.2020.109692. Epub 2020 Feb 25.

Abstract

The purpose of this study was to investigate the effect of fatigue on selected lower extremity muscles synergy during running using non-negative matrix factorization algorithm method. Sixteen male recreational runners participated in this study. The surface electromyographic activity of rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM), biceps femoris (BF), semitendinosus, gastrocnemius medialis (GM), soleus (SO) and tibialis anterior (TA) were recorded on treadmill at 3.3 m s before and after the fatigue protocol. Synergy pattern and relative muscle weight were calculated by non-negative matrix factorization (NNMF) algorithm method. The results showed that using the VAF method, five muscle synergies were extracted from the emg data during running. After the fatigue, the number of muscular synergies did not show a change, but relative weight of the muscles changed. Fatigue did not have any effect on the structure of muscular synergy, but changed the relative weight of muscles. These changes could be the strategy of the central nervous system to maintain optimal function of the motor system.

摘要

本研究旨在利用非负矩阵分解算法方法研究疲劳对跑步时选定下肢肌肉协同作用的影响。16 名男性休闲跑步者参与了这项研究。在疲劳方案之前和之后,在跑步机上记录了股直肌(RF)、股外侧肌(VL)、股内侧肌(VM)、股二头肌(BF)、半腱肌、腓肠肌内侧(GM)、比目鱼肌(SO)和胫骨前肌(TA)的表面肌电图活动。通过非负矩阵分解(NNMF)算法方法计算协同模式和相对肌肉重量。结果表明,在跑步过程中,使用 VAF 方法从 emg 数据中提取了五个肌肉协同作用。疲劳后,肌肉协同作用的数量没有变化,但肌肉的相对重量发生了变化。疲劳对肌肉协同作用的结构没有影响,但改变了肌肉的相对重量。这些变化可能是中枢神经系统维持运动系统最佳功能的策略。

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