基于肌电特征K值的穿戴式膝关节运动疲劳的监测系统研究

Research on Wearable Knee Sports Fatigue Monitoring System Based on EMG Characteristic K-Value

为了对运动和康复训练过程中膝关节疲劳的评定提供科学依据,对肌疲劳引起的继发关节损伤起到预防作用,开发了基于表面肌电(sEMG)信号的穿戴式膝关节运动疲劳监测系统。通过采集受试者在疲劳前后股四头肌的sEMG信号,对其采用基于样本熵的经验模态分解法去噪,分析均方根(RMS)、样本熵(S)和肌电特征K值在肌疲劳前后的变化。结果表明:从非疲劳状态进入疲劳状态,且疲劳程度不断加深这一过程中,RMS呈递增变化;S呈递减变化,变化趋势较小;K呈递增变化,与RMS和S相比变化趋势更加明显;三种特征参数均能反映肌肉疲劳状态,其中,肌电特征K值对肌肉疲劳状态的表征效果最好,肌疲劳前后的显著性差异P值最小;且算法具有运算量小、实时性好的特点,适用于对运动过程中产生的膝关节疲劳进行实时精准评估与预警。

In order to provide a scientific basis for the assessment of knee joint fatigue during exercise and rehabilitation training, and to prevent secondary joint damage caused by muscle fatigue, a wearable knee joint exercise fatigue monitoring system based on surface electromyography (sEMG) signals has been developed. By collecting the sEMG signals of the subjects’ quadriceps before and after fatigue, the empirical mode decomposition method based on sample entropy is used to denoise them, and the root mean square (RMS), sample entropy (S) and EMG characteristic K-value are analyzed changes before and after muscle fatigue. The results show that in the process of entering from a non-fatigue state to a fatigue state, and the degree of fatigue continues to deepen, the RMS changes gradually;S changes gradually, with a smaller change trend;K changes gradually, which is more changing than RMS and S obvious;the three characteristic parameters can reflect the state of muscle fatigue. Among them, the electromyographic characteristic K-value is the best in characterizing the state of muscle fatigue, and the significant difference between before and after muscle fatigue is the smallest;and the algorithm has a small amount of calculation and real-time performance good features, suitable for real-time accurate assessment and early warning of knee joint fatigue during exercise.