阻力训练数学模型的建立与验证

Shougo Hatanaka, N. Ishii
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引用次数: 1

摘要

提出并验证了一套具有重复动态和自主激活膝伸肌的阻力训练数学模型(RT模型)。RT模型预测肌肉活动、疲劳和恢复,可用于预测不同肌肉纤维类型中识别的机械脉冲。本研究涉及几个阻力训练程序变量(如负荷、速度、组数和休息时间)。来自六名受试者的实验数据是在几种不同的训练方案下获得的。对于所研究的方案,该模型精确预测了伸膝阻力运动期间的最大重复次数(R2=0.87)。该研究证明了第一个简单数学模型对膝伸肌自主动态运动的适用性。所开发的模型计算了所选方案的快速抽动纤维的机械脉冲。例如,该模型显示,在没有失败的情况下,高强度训练往往比低强度训练诱导更多的快速抽搐纤维募集。该模型有望帮助理解不同纤维类型的脉冲如何影响训练。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Proposal and validation of mathematical model for resistance training
A set of mathematical models for resistance training (RT model) with repeated dynamic and voluntary activations of knee extensor muscles was proposed and validated. RT model predicts muscle activity, fatigue and recovery, and can be used to predict the mechanical impulses identified in different muscle fiber types. Several resistance training program variables (e.g. load, velocity, number of sets, and duration of rest intervals) were addressed in this study. Experimental data from six subjects were taken under several different training protocols. For the protocols studied, the model precisely predicted the maximum number of repetitions during knee extension resistance exercise (R2 = 0.87). This study demonstrates the applicability of the first simple mathematical model for voluntary dynamic exercise of knee extensor muscles. The developed model calculates the mechanical impulse of fast twitch fiber for the selected protocols. For instance, the model shows high intensity training tends to induce more fast-twitch fiber recruitment than that in low intensity training in the case of non-failure. This model is expected to help in understanding how impulses of different fiber types contribute and affect training.
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