[The difference of surface electromyography data processing method based on simulated manal-lifting-task].

To study the differences of different signal processing method of surface electromyography (sEMG) in judging muscle fatigue. From July to October 2019, based on the model of simulated manual lifting operation, the original sEMG signals from 13 volunteers of brachial radial muscle, brachial two-headed muscle, triangle muscle, left vertical spine muscle, right vertical spine muscle and lateral femoral muscle were collected in the operation activities. Three different electromyography signal processing methods (all signal from motion beginning to the end, peak signal and ehe specified motion signal) were used to analyze the original data in time domain (RMS) and frequency domain (MDF) , the data difference between different electromyography signal processing methods was analyzed by using Wilcoxon rank and sum test and nonlinear curve fitting method. The age of the subjects of the simulated lifting operation was (24.31±2.02) years old, height (173.78±4.84) cm, weight (66.28±5.58) kg, body mass index (BMI) 21.94±1.58. The thickness of triceps skinfold was (14.08±4.86) mm, and the thickness of the skin fold under the scapula was (15.54±3.59) mm. After processing the original signal data by using different sEMG signal interception methods, the normality test, Levene's test, and the Wilcoxon test showed that, except for the MDF index of the brachial two-headed muscle, the differences in the RMS and MDF signals of the other muscles were statistically significant (<0.016) . The all signal processing method dealed with data distribution dispersion better than other methods, and the rate of change of RMS signal slope was higher than other methods. Non-linear regression results showed that all signal processing method had low volatility in processing data, and the regression equation had a high degree of fit. Different electromyography signal processing methods have differences. The all signal processing method which intercepts from starting point to the end point of action cycle has the least data volatility, and electromyography time domain and frequency domain index with the highest sensitivity of time, which is suitable for the application of surface electromyography to judge muscle fatigue in dynamic and complex operations.