Abstract
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.
Highlights
Smart cars with biometrics technology are recently evolving to identify drivers and provide services in the vehicle environment [1,2]
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
In order to normalize the morphological features of the ECG signal measured in the complex state to a distinct ECG cycle, we propose a method in which the ideal ECG cycle of each subject is selected, and the recognition data is filtered to the ideal ECG and normalized
Summary
Smart cars with biometrics technology are recently evolving to identify drivers and provide services in the vehicle environment [1,2]. In order to identify a driver from outside the vehicle, initially, it was attempted to open the door of the vehicle and start the vehicle with physical security systems based on ownership Later, it was developed into a technology with simple personal customized security systems using the driver’s bio-information that does not risk loss [5]. Like driver security technology, personalized services are provided by driver recognition technology using the driver’s bio-information inside the vehicle [6]. 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.
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