ECG 기반의 운전자별 인지 부하 평가 방법 개발

Abstract

Objective: The present study is to develop an effective evaluation method for a driver’s cognitive workload using electrocardiography (ECG) signal. Background: ECG measures such as heart rate (HR) and heart rate variability (HRV) have been used for cognitive workload evaluation. Since ECG changes by cognitive workload vary largely depending on personal characteristics, an optimal analysis protocol of ECG needs to be tailored to each individual accordingly; however, existing studies have not considered personal characteristics in ECG analysis for cognitive workload evaluation. Method: The proposed evaluation method uses the area under the receiver operating characteristic (ROC) curve (AUC). A preliminary analysis was conducted with ECG data collected in a driving simulator while an n-back task was conducted. AUC analysis was performed for four ECG metrics (mean IBI, SDNN, RMSSD, and RMSE), three window spans (20, 30, and 40 seconds), and three update rates (1, 2, and 3 seconds). Results: It was identified that the optimal ECG analysis parameters of metric, window span, and update rate maximizing the discriminability of cognitive workload evaluation varied between individual drivers. Conclusion: The finding of the present study supports the use of an individually customized ECG analysis protocol for better evaluation accuracy of a drivers’ cognitive workload. Application: The proposed ECG analysis method for cognitive workload evaluation can be applied to development of a safe driving support system.