面向铁路行车调度员疲劳识别的动作检测

Train Operation command and dispatch is the core monitoring position of railway transportation, and detecting the fatigue Status of dispatchers is of great significance for ensuring railway Operation safety. A railway dispatcher fatigue action recognition method based on bidirectional long and short-term memory neural network and support vector machine adaptive enhancement algorithm is proposed to reduce the human factor risks in safety production. Using a High-Resolution Network (HRNet) human key point detection model to extract multiple human key points, the angle and length ratio features of human action behavior were extracted. An action recognition model based on a Bi-directional Long Short-Time Memory-Support Vector Machine (BiLSTM - SVM) was constructed. The model parameters were optimized using orthogonal experimental methods, Finally, an adaptive boosting algorithm (Adaboost, Adaptive Boosting) was adopted to further enhance fatigue action recognition. Using accuracy, recall, precision, and Fx Score as the model evaluation metrics, we performed the ablation experiment on LSTM, BiLSTM, and BiLSTM - SVM. The experimental results show that BiLSTM — SVM — Adaboost yields the best Performance. The accuracy of the prediction model is 0. 96, which is respectively 0. 12, 0. 04, and 0. 02 higher than that of the comparative model. The Recall of the prediction model is 0.96, which is respectively 0. 12, 0.03, and 0.02 higher than that of the comparative model. The Precision of the prediction model is 0. 97, which is respectively 0. 08, 0. 03, and 0. 02 higher than that of the comparative model. The Fx Score call of the prediction model is 0. 96, which is respectively 0. 14, 0. 04, and 0. 03 higher than that ol the comparative model. The experimental results show that the BiLSTM — SVM — Adaboost algorithm on the scheduling Simulation fatigue action dataset achieved an accuracy and precision of 0. 96 and 0. 97, compared to the " Openpose + ANN" and " DBN + LSTM" network algorithms, there is a Performance improvement. The model improves the accuracy of human fatigue movement Classification, providing a basis for fatigue detection of dispatchers.