Driver Identification System Using Normalized Electrocardiogram Based on Adaptive Threshold Filter for Intelligent Vehicles

Choi, Gyu Ho; Lim, Kiho; Pan, Sung Bum

doi:10.3390/s21010202

Cited by 16 publications

(9 citation statements)

References 49 publications

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“…A normalized electrocardiogram (ECG) based adaptive threshold filter approach was also presented, which measures the ECG while the subject is sitting, touching a slide, and after the subject has finished exercising. The results of the studies show that the suggested method increased average similarity when compared to results obtained without the normalization step [32].…”

Section: Driver Identity Recognitionmentioning

confidence: 92%

Physiological-based Driver Monitoring Systems: A Scoping Review

et al. 2022

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A physiological-based driver monitoring system (DMS) has attracted research interest and has great potential for providing more accurate and reliable monitoring of the driver’s state during a driving experience. Many driving monitoring systems are driver behavior-based or vehicle-based. When these non-physiological based DMS are coupled with physiological-based data analysis from electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), and electromyography (EMG), the physical and emotional state of the driver may also be assessed. Drivers’ wellness can also be monitored, and hence, traffic collisions can be avoided. This paper highlights work that has been published in the past five years related to physiological-based DMS. Specifically, we focused on the physiological indicators applied in DMS design and development. Work utilizing key physiological indicators related to driver identification, driver alertness, driver drowsiness, driver fatigue, and drunk driver is identified and described based on the PRISMA Extension for Scoping Reviews (PRISMA-Sc) Framework. The relationship between selected papers is visualized using keyword co-occurrence. Findings were presented using a narrative review approach based on classifications of DMS. Finally, the challenges of physiological-based DMS are highlighted in the conclusion. Doi: 10.28991/CEJ-2022-08-12-020 Full Text: PDF

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Section: Driver Identity Recognitionmentioning

confidence: 92%

Physiological-based Driver Monitoring Systems: A Scoping Review

et al. 2022

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“…e matching of motion trajectory is to use the Mahalanobis distance of the positioning position predicted in the filter by the detection result and tracking result to express its degree [14]. e equation is as follows:…”

Section: General Video Feature Extraction Methods Based On DLmentioning

confidence: 99%

“…The state of an object is to describe the trajectory state at a certain time in an 8-dimensional space. The matching of motion trajectory is to use the Mahalanobis distance of the positioning position predicted in the filter by the detection result and tracking result to express its degree [ 14 ]. The equation is as follows:

y i is the predicted observation of the trajectory, d j is the result state of the j -th, and S i is the covariance matrix obtained through the filter.…”

Section: Construction and Application Analysis Of Moving Target Detec...mentioning

confidence: 99%

Design of Moving Target Detection System Using Lightweight Deep Learning Model and Its Impact on the Development of Sports Industry

Zhang

Zheng

2022

Computational Intelligence and Neuroscience

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The intelligent tracking and detection of athletes’ actions and the improvement of action standardization are of great practical significance to reducing the injury caused by sports in the sports industry. For the problems of nonstandard movement and single movement mode, this exploration takes the video of sports events as the object and combines it with the video general feature extraction of convolutional neural network (CNN) in the field of deep learning and the filtering detection algorithm of motion trajectory. Then, a target detection and tracking system model is proposed to track and detect targets in sports in real-time. Moreover, through experiments, the performance of the proposed system model is analyzed. After testing the detection quantity, response rate, data loss rate, and target detection accuracy of the model, the results show that the model can track and monitor 50 targets with a loss rate of 3%, a response speed of 4 s and a target detection accuracy of 80%. It can play an excellent role in sports events and postgame video analysis, and provide a good basis and certain design ideas for the goal tracking of the sports industry.

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“…Choi et al [43] used a continuous firstorder regression analysis method to remove baseline wondering noise, maintaining morphological features and adjusting them to zero. In addition, a normalization study was conducted in which the morphological features of the ideal ECG cycle P, QRS Complexes, and T waves were clearly generated [44]. Rehman et al [45] confirmed the fiducial point segmentation method using the P, QRS Complexes, and T waves features of the ECG's one cycle with higher identification accuracy than the nonfiducial point segmentation method.…”

Section: Driver Identification System Usingmentioning

confidence: 99%

Identification System Based on Resolution Adjusted 2D Spectrogram of Driver’s ECG for Intelligent Vehicle

Choi

Lim

Pan

2022

Mobile Information Systems

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Recently, traditional vehicles are being developed into intelligent vehicles as information is exchanged among various devices inside and outside the vehicles. In the connected car environment, the need for vehicle security is growing due to vehicle hacking accidents and possible threats to human life. Driver identification technology using electrocardiogram (ECG) signals has been studied to address vehicle security issues and driver-specific services. Existing driver identification systems tried to address the issues using a multidimensional feature extraction method. However, there are remaining issues, including accuracy concerns, because the resolution was adjusted without considering the ECG’s P, QRS Complexes, and T waves feature when analyzing the time-frequency multidimensional features. In this paper, we propose a driver identification system using a 2D spectrogram. It identifies a section where the resolution is optimally adjusted using a spectrogram that can simultaneously analyze the time-frequency features of an ECG. The experimental results show that the proposed method improved the identification performance compared to the existing multidimensional feature extraction methods such as EEMD and MFCCs. Besides, with a 2D spectrogram of 1/4 image size, the recognition performance is maintained in a CNN network and the training time is significantly reduced.

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Driver Identification System Using Normalized Electrocardiogram Based on Adaptive Threshold Filter for Intelligent Vehicles

Cited by 16 publications

References 49 publications

Physiological-based Driver Monitoring Systems: A Scoping Review

Physiological-based Driver Monitoring Systems: A Scoping Review

Design of Moving Target Detection System Using Lightweight Deep Learning Model and Its Impact on the Development of Sports Industry

Identification System Based on Resolution Adjusted 2D Spectrogram of Driver’s ECG for Intelligent Vehicle

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