Real-time monitoring and prediction of remote operator fatigue in plateau deep mining based on dynamic Bayesian networks.

Fatigue can cause human error, which is the main cause of accidents. In this study, the dynamic fatigue recognition of unmanned electric locomotive operators under high-altitude, cold and low oxygen conditions was studied by combining physiological signals and multi-index information. The characteristic data from the physiological signals (ECG, EMG and EM) of 15 driverless electric locomotive operators were tracked and tested continuously in the field for 2 h, and a dynamic fatigue state evaluation model based on a first-order hidden Markov (HMM) dynamic Bayesian network was established. The model combines contextual information (sleep quality, working environment and circadian rhythm) and physiological signals (ECG, EMG and EM) to estimate the fatigue state of plateau mine operators. The simulation results of the dynamic fatigue recognition model and subjective synchronous fatigue reports were compared with the field-measured signal data. The verification results show that the synchronous subjective fatigue and simulated fatigue estimation results are highly consistent (correlation coefficient r = 0.971**), which confirms that the model is reliable for long-term dynamic fatigue evaluation. The results show that the established fatigue evaluation model is effective and provides a new model and concept for dynamic fatigue state estimation for remote mine operators in plateau deep mining. Moreover, this study provides a reference for clinical medical research and human fatigue identification under high-altitude, cold and low-oxygen conditions.