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稳定与非稳定状态下负重与肌肉活性关系模型的构建与实证 被引量:14

Construction and Empirical Research on Relationship Model between Load and Muscle Activity under Stable and Unstable States
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摘要 目的:探讨人体负重与肌肉活性之间的关系。方法:运用肌电测试法对30名受试者在稳定与非稳定状态下不同负重的深蹲相关肌肉做肌电测试实验。结果:①不同状态下负重与肌肉活性均表现为显著的正相关关系(稳定r=0.700,P<0.000;非稳定r=0.821,P<0.000;整体r=0.773,P<0.000)。②稳定与非稳定状态对人体肌肉活性的刺激存在差异,但这种差异会随着负重的提升而发生变化。③各线性模型(调整R^2=0.864~0.914)与非线性模型(调整R^2=0.876~0.931)均拟合较好。结论:人体在稳定与非稳定状态下负重与肌肉活性的关系更符合一种S型曲线关系模型,其关系既受"特征形式"的影响,也受"生物学"因素的干扰,并存在"主导因素"的转化。实证发现,S型曲线模型能够较好地弥补线性模型的高估现象。 To investigate the relationship between load and muscle activity.Methods:The study designed an EMG test,i.e.,30 subjects were tested in deep squat with different loads under both stable(ground)and unsteady(balance Plate)states.Results:(1)Load and muscle activity under different states were showed significantly positive correlation(steady state:r=0.700,P﹤0.000;unsteady state:r=0.821,P﹤0.000;whole state:r=0.773,P﹤0.000).(2)A certain difference exists in the stimulus degree of the body muscle activity under stable and unstable states,but varies with the increase of load.(3)The linear model(adjusted R^2=0.864~0.914)and the nonlinear model(adjusted R^2=0.876~0.931)were both fitted well.Conclusion:The relationship between load and muscle activity in both stable and unstable states may be in line with an“S”curve relationship model,and this model is affected by both the“characteristic forms”and the“biological”factors,and there is also the dominant factor transformation.The empirical study indicates that the“S”curve model can better compensate for the overestimation of the linear model.
作者 董德朋 袁雷 DONG Depeng;YUAN Lei(School of Philosophy and Society,Jilin University,Changchun 130012,Jilin,China;School of Physical Education,Jilin University,Changchun 130012,Jilin,China)
出处 《上海体育学院学报》 CSSCI 北大核心 2019年第1期119-126,共8页 Journal of Shanghai University of Sport
基金 国家体育总局奥运攻关项目(2016HT052) 吉林省高教学会高教科研课题(JGJX2017D8) 吉林大学博士研究生交叉学科科研资助计划项目(10183201802)
关键词 负重 肌肉活性 线性 非线性 S型曲线 load muscle activity linearity nonlinearity S curve
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