Recognition model of miner fatigue degree based on genetic algorithm optimized by least squares support vector machine

In order to accurately identify the fatigue degree of miners and reduce the accidents caused by miners' fatigue, a recognition model of mine fatigue degree based on genetic algorithm (GA) optimized by the least squares support vector machine (LSSVM) was proposed. First, the ECG data of miners were collected through fatigue induction experiments, and Friedman test was used to optimize the characteristic indicators of miners' fatigue degree. Then, the principal component analysis(PCA) was used to reduce the dimension of the selected feature indexes, and the feature parameter set representing the fatigue degree of the miner was established. On this basis, the key parameters of the least squares support vector machine were optimized by genetic algorithm, and the fatigue degree recognition model of miners was constructed. The results show that the selected characteristic indexes of miners' fatigue degree can effectively reflect the miners' fatigue degree. Compared with GA-SVM and LSSVM models, the fusion of GA-LSSVM model can significantly improve the recognition accuracy of miners' fatigue degree (the average recognition accuracy is 96.87%). The fatigue degree recognition model constructed can identify the fatigue degree of miners more efficiently, and has certain practical guiding significance for the prevention and control of coal mine accidents.