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目的为提高采用表面肌电信号(sEMG)进行肌力预测的通用性,探索肌疲劳过程中肌力预测模型的修正方法。方法分别对肌肉非疲劳状态和疲劳状态下的肌力预测问题进行研究。首先采用曲线拟合建立非疲劳状态下的肌力预测模型,然后开展恒力和线性上升力两种发力方式的疲劳实验,分析肌疲劳对肌力预测的影响,并在此基础上针对线性上升力疲劳过程,建立肌力预测模型系数与肌疲劳程度的函数关系,最后利用疲劳程度对肌力预测模型进行动态修正。结果测试结果显示,当疲劳程度逐渐加深时修正后的预测误差基本保持稳定。结论本方法可有效用于线性上升力疲劳过程的肌力预测。
Objective To improve the generality of predicting muscle strength by surface electromyography (sEMG), and to explore the correction method of muscle force prediction model during muscle fatigue. Methods The muscle strength prediction under non-fatigue state and fatigue state were studied respectively. First of all, using curve fitting to establish the muscle strength prediction model under non-fatigue state, and then carrying out the fatigue experiment of constant forces and linear ascending forces, and analyzing the effect of muscle fatigue on muscle strength prediction. Based on this, Uplift force fatigue process, the establishment of muscle strength prediction model coefficients and muscle fatigue as a function of the relationship between the degree of fatigue Finally, dynamic correction of muscle strength prediction model. Results The test results show that when the degree of fatigue deepens, the revised prediction error remains basically stable. Conclusion This method can be effectively applied to the prediction of muscle strength in the process of linear ascending force fatigue.