Resumen
Seafarers are prone to reduce behavioral reliability under high workloads, resulting in human errors and accidents. To explore the changes in seafarers? workload and physiological activities under complex task conditions, a bridge simulator experiment was conducted to collect the EEG and ECG data of 23 seafarers. The power in different EEG sub-bands was extracted from a one-channel EEG acquisition headset employed by Welch?s method and ratio processing. The features such as root mean square of RR interval difference (RMSSD) were extracted from ECG. Then, an improved seafarer workload recognition method based on EEG combined with ECG and complex task scenarios was proposed, and the performance of the machine learning algorithm was evaluated by cross-validation. Compared with the recognition model that only uses the task scenarios as the workload calibration, the EEG recognition model based on the workload level calibrated by the ECG and the task scenarios is more effective, with an accuracy rate of 92.5%, an increase of 25.9%. The results show that the improved workload recognition model can effectively identify seafarers? workload, and the model trained with the bagging algorithm has the best performance. Furthermore, time domain features of EEG and ECG fluctuate regularly with the task scenarios? complexity. The research results can develop online intelligent monitoring, and human?computer interaction active early warning technology and equipment.