Development of a vigilance monitoring and intervention wearable device for high-speed train drivers
[Objective]High-speed rail(HSR)accidents are largely attributed to human errors made by train drivers.HSR drivers frequently encounter situations that induce fatigue during operations,leading to a decrease in their vigilance.Therefore,conducting online research on changes in HSR drivers'vigilance and implementing timely fatigue interventions are crucial to ensure that they can safely and attentively perform their tasks.[Methods]This paper proposes a novel method for identifying and addressing driver fatigue by leveraging electroencephalogram(EEG)signal characteristics and develops a wearable alertness intervention device that integrates real-time monitoring,fatigue detection,and intelligent intervention.This system includes a collection terminal,web-based fatigue detection equipment,fatigue intervention modules,and Bluetooth microcontrollers.This study involved collecting real-time fatigue EEG data utilizing an eight-channel EEG apparatus within the Stroop fatigue induction paradigm,which was then transmitted via Wi-Fi to a web-based platform.The OPENBCI open-source software converted EEG waveforms into digital signals.MATLAB software employed the Welch algorithm to extract seven key EEG indicators,including five basic waveform powers,power ratios,and comprehensive feature values.These indicators were analyzed alongside subjective fatigue assessments(KSS values)and objective reaction times to examine variations in driver EEG waves.The grey correlation analysis method was employed to determine the weight of each EEG indicator with respect to objective reaction time.A KSS value of 5 or higher was used as the fatigue benchmark,with a weighted average establishing a reaction time of 1.118s as the criterion for fatigue evaluation.The random forest algorithm,implemented in Python with hyper parameter optimization via grid search,was used for weighted feature extraction of fatigue EEG indicators to establish fatigue threshold values for real-time fatigue recognition.Intervention commands were transmitted to the main control circuit board of the device via LoRa remote communication.This paper designed and developed physical intervention modules for smell,sound,vibration,and electrical stimulation that were controlled and integrated using double-layer PCB circuit boards.These were ultimately integrated with a flexible wearable alertness intervention device that provides diverse and personalized intervention effects.Additionally,we designed an alertness warning platform to visualize the driver's alertness status and fatigue interventions.[Results]According to experimental data,the EEG-based fatigue detection classifier demonstrated over 90％accuracy on the test set.Each intervention module showed consistent communication performance and effectively boosted alertness.The system maintained stable operation for more than 2.5 hours.[Conclusions]This paper presents the design of a portable,wearable alertness intervention device leveraging online EEG signal detection technology to monitor and intervene in HSR fatigue.It offers a thorough solution to address fatigue-related issues in HRS drivers.The study's findings provide valuable theoretical and practical guidelines for designing and implementing fatigue intervention devices,enhancing driver safety and performance.Ultimately,these results offer fresh data references for rostering and duty planning,with significant potential for further advancement and use.
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