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In order to ensure the personal safety of the drivers and passengers of railtransit in plateau environment, the vital signs and train conditions of thedrivers and passengers are taken as the research object, and the dynamicrelationship between them is studied and analyzed. In this paper, subwaydrivers under normal operation conditions are taken as research objects toestablish the vital signs monitoring and early warning system. The vital signsdata of the subway drivers, such as heart rate (HR), respiratory rate (RR),body temperature (T) and blood oxygen saturation (SPO2) of the subway driverare collected by the head-mounted sensor, and the least mean square adaptivefiltering algorithm is used to preprocess the data and eliminate theinterference information. Based on the improved BP (Back Propagation) neuralnetwork algorithm, a prediction model is established to predict the vital signsof subway drivers in real-time. We use the early warning score evaluationmethod to measure the risk of subway drivers' vital signs, and then thenecessary judgment basis can be provided to dispatchers in the control center.Experiments show that the system developed in this paper can accurately predictthe evolution of subway drivers' vital signs, and timely warn the abnormalstates. The predicted value of vital signs is consistent with the actual value,and the absolute error of prediction is less than 0.5 which is within theallowable range.

Real-time Monitoring and Early Warning Analysis of Urban Railway  Operation Based on Multi-parameter Vital Signs of Subway Drivers in Plateau  Environment