Investigation of Three Potential Stress Inducement Tasks During On-Road Driving

Mathissen, Marcel; Hennes, Nikica; Faller, Fabian; Leonhardt, Steffen; Teichmann, Daniel

doi:10.1109/tits.2021.3112811

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…Several studies have used HR and HRV from the ECG as primary data to investigate the driver's condition. For example, Mathissen et al [21] investigate tasks that could potentially induce stress in the driver to assess the driver's condition. These tasks included sequential memory tasks, sing-a-song stress tests, and noise exposure tests.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Physiological State of Drivers Using In-Vehicle Sensing of Non-Invasive Signal

Abdul Razak,

Sayed Ismail,

Hii Ben Bin

et al. 2024

Civ Eng J

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Eighty percent of traffic accidents are caused by human error, called hypo vigilance, stemming from drowsiness, stress, or distraction while driving. This poses a significant threat to road safety. An electrocardiogram (ECG) is often used to monitor drivers' health. Thus, enhancing vehicles with Internet of Things (IoT) sensors and local analytical databases becomes crucial for real-time detection and transmission of relevant health data to avoid things that compromise road traffic safety. This study introduces a cost-effective in-vehicle ECG sensing prototype using an AD8232 sensor integrated with an Arduino Uno and an AD8232 Wi-Fi module placed on the steering wheel to monitor the driver's heart signal while driving. Short-term heart rate variability (HRV) features were computed through Python from the acquired ECG data, and supervised machine learning techniques such as AdaBoost, Random Forest, Naïve Bayes, and Support Vector Machine (SVM) classified the features into normal and abnormal classes. Naive Bayes exhibited the highest accuracy (90.91%) and F1 score (85.71%), surpassing Random Forest's lower accuracy (63.64%) and F1 score (50.00%). These findings indicate the prototype's potential as a valuable tool for ensuring safe and efficient driving, proposing integration into standard vehicle safety systems for enhanced road traffic safety. Doi: 10.28991/CEJ-2024-010-04-014 Full Text: PDF

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Section: Introductionmentioning

confidence: 99%

Monitoring Physiological State of Drivers Using In-Vehicle Sensing of Non-Invasive Signal

Abdul Razak,

Sayed Ismail,

Hii Ben Bin

et al. 2024

Civ Eng J

View full text Add to dashboard Cite

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“…Using these sensors, the driver's physiological status can be extracted by means of their heart rate, respiratory rate, heart rate variability (HRV) and oxygen saturation [18,19]. These values can be analysed to detect stress, drowsiness, heart attacks, strokes or atrial fibrillation [20][21][22]. If monitored continuously over time, trends with respect to the overall health status and the early detection of physiological complications can be assessed.…”

Section: Introductionmentioning

confidence: 99%

A Portable Multi-Modal Cushion for Continuous Monitoring of a Driver’s Vital Signs

Linschmann

Uguz

Romanski

et al. 2023

Sensors

Self Cite

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With higher levels of automation in vehicles, the need for robust driver monitoring systems increases, since it must be ensured that the driver can intervene at any moment. Drowsiness, stress and alcohol are still the main sources of driver distraction. However, physiological problems such as heart attacks and strokes also exhibit a significant risk for driver safety, especially with respect to the ageing population. In this paper, a portable cushion with four sensor units with multiple measurement modalities is presented. Capacitive electrocardiography, reflective photophlethysmography, magnetic induction measurement and seismocardiography are performed with the embedded sensors. The device can monitor the heart and respiratory rates of a vehicle driver. The promising results of the first proof-of-concept study with twenty participants in a driving simulator not only demonstrate the accuracy of the heart (above 70% of medical-grade heart rate estimations according to IEC 60601-2-27) and respiratory rate measurements (around 30% with errors below 2 BPM), but also that the cushion might be useful to monitor morphological changes in the capacitive electrocardiogram in some cases. The measurements can potentially be used to detect drowsiness and stress and thus the fitness of the driver, since heart rate variability and breathing rate variability can be captured. They are also useful for the early prediction of cardiovascular diseases, one of the main reasons for premature death. The data are publicly available in the UnoVis dataset.

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