Why the study?
Existing ECG normalization methods distort morphological features like P, QRS, and T waves or normalize noisy signals, while motion artifact noise impairs driver identification in intelligent vehicles.
An adaptive threshold filter-based normalization method improves ECG-based driver identification performance in driving environments by reducing motion artifact distortion.
May support ECG driver identification amid motion artifacts; leaves open prospective validation before deployment.
Driver-centered infotainment and telematics services are provided for intelligent vehicles that improve driver convenience. Driver-centered services are performed after identification, and a biometrics system using bio-signals is applied. The electrocardiogram (ECG) signal acquired in the driving environment needs to be normalized because the intensity of noise is strong because the driver's motion artifact is included. Existing time, frequency, and phase normalization methods have a problem of distorting P, QRS Complexes, and T waves, which are morphological features of an ECG, or normalizing to signals containing noise. In this paper, we propose an adaptive threshold filter-based driver identification system to solve the problem of distortion of the ECG morphological features when normalized and the motion artifact noise of the ECG that causes the identification performance deterioration in the driving environment. The experimental results show that the proposed method improved the average similarity compared to the results without normalization. The identification performance was also improved compared to the results before normalization.
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Choi et al. (2020) studied this question.
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