Key result
An intelligent system using ECG return mapping features distinguished driver stress levels in various driving situations with an accuracy of 98 ± 2%.
Why the study?
Stress and anxiety negatively impact cardiovascular disease and increase accident risk, motivating the development of a system to detect driver stress across various driving situations.
Observational (n=10)
An algorithm using linear and non-linear ECG features can accurately detect driver stress levels, potentially enabling intelligent systems to warn drivers and improve road safety.
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ECG-based driver stress detection shows promise; leaves open prospective validation before clinical adoption.
In addition to the devastating effects of anxiety and stress on the development and exacerbation of the cardiovascular disease, lack of stress control increases drivers' risk of accidents. This paper aims to identify the stress of drivers in various driving situations to warn the driver to control the tense conditions during driving. In order to detect stress while driving, we used the heart signals in the Physionet database. To analyze the conditions of the electrocardiogram (ECG) under various driving situations, linear and non-linear features were used. The characteristics of the RRIs are the only able to identify driver stress in different driving modes relative to rest periods, while the return mapping features, in addition to identifying driver stress while resting, have the ability to identify stress between different driving positions also brought. The results showed that driver's stress level during driving in city 1 and highway 1 with a P-value of 0.028 and also in city 3 and highway 2 with a P-value of 0.041 can be distinguished. The accuracy obtained from the proposed detection method is 98±2% for 100 iterations. The result indicated an efficiency of our proposed method and enhanced the reliability.
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Moridani et al. (2021) conducted an observational in Driver stress (n=10). ECG signal analysis (return map features) vs. Rest periods was evaluated on Accuracy of driver stress detection. An intelligent system using ECG return mapping features distinguished driver stress levels in various driving situations with an accuracy of 98 ± 2%.
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