Research on work-stress recognition for deep ground miners based on depth-separable convolutional neural network

Abstract

In deep mine production operations, the challenging operating environment intensifies the workload and pressure on coal miners. Long-term exposure to high-intensity operating pressure can seriously impact the physical and mental health of miners, leading to unsafe behaviors and accidents. To identify the pressure of miners' operations, this paper examines various driving scenarios, such as the deep-well tunneling machine cutting the wall and opening the alley, the shoveling machine shoveling ore, and the pickup truck driver transporting. The paper randomly collects facial images of miners during each operation using an explosion-proof CCD camera to obtain the facial expression characteristic data of miners. The Ferface2013 facial expression dataset was used to establish the dataset. The depth separable convolutional neural network MiniXception was used for training and to output the classification results of the pressure degree of deep shaft miners. A MiniXception-based miners' operating pressure recognition model was established. The training time, precision, recall, F1 score, and classification accuracy confusion matrix were selected. The study evaluated the effectiveness of the recognition model by measuring its training time, precision, recall, F1 score, and classification accuracy confusion matrix. The results indicate that the model has a correct recognition rate of 88% for the pressure state, 91% for the pleasure state, and 74% for the normal state. The overall accuracy of the model is 0.843. Therefore, the MiniXception recognition model is suitable for recognizing the pressure of miners' operations in deep mines. This can meet practical needs and is useful for preventing major accidents in mines, managing on-site safety, and managing safety in non-hazardous areas. It has important theoretical and practical significance.