Detecting method for muscle fatigue level and equipment

Abstract

The invention is applicable to the technical field of information processing, and provides a detecting method for a muscle fatigue level and equipment. The method includes the following steps: obtaining a biometric signal of a user; obtaining a wavelet amplitude mean value of a hemoglobin concentration signal through a preset wavelet averaging algorithm; performing Fourier transform on a surface myoelectric signal to obtain a surface myoelectric frequency domain curve of the surface myoelectric signal, and determining a median frequency value according to the surface myoelectric frequency domain curve; leading the wavelet amplitude mean value, median frequency value and a blood oxygen saturation concentration signal mean value into a preset fatigue level calculation model, and determininga current fatigue characteristic value of the user; and based on a preset fatigue level index table, determining the fatigue level corresponding to the fatigue characteristic value. According to the method, the active state of muscle fibers of current user muscles can be determined through the myoelectric signal, and the metabolic state of the muscles can be determined through the blood oxygen signal, so that a more comprehensive judgment on the muscle fatigue state is realized, and the detection accuracy of the muscle fatigue level is improved. 本发明适用于信息处理技术领域，提供了一种肌肉疲劳等级的检测方法及设备，包括：获取用户的生物特征信号；通过预设的小波平均算法，获取血红蛋白浓度信号的小波振幅均值；对表面肌电信号进行傅里叶变换，得到表面肌电信号的表面肌电频域曲线，并根据表面肌电频域曲线确定中位频率值；将小波振幅均值、中位频率值以及血氧饱和浓度信号的均值，导入预设的疲劳度计算模型，确定用户当前的疲劳特征值；基于预设的疲劳等级索引表，确定疲劳特征值对应的疲劳等级。本发明通过肌电信号可以确定当前用户肌肉的肌肉纤维的活性状态，而通过血氧信号则可以确定肌肉的代谢状态，从而对肌肉疲劳状态有一个较为全面的判定，提高了肌肉疲劳等级的检测准确性。