The International Arab Journal of Information Technology (IAJIT)


Designing an Intelligent System to Detect Stress

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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[30] Vargas-Lopez O., Perez-Ramirez C., Valtierra- Rodriguez M., Yanez-Borjas J., and Amezquita- Sanchez J., “An Explainable Machine Learning Approach Based on Statistical Indexes and SVM for Stress Detection in Automobile Drivers Using Electromyographic Signals,” Sensors, vol. 21, no. 9, pp. 3155, 2021. Mohammad Karimi received a BS degree in electrical engineering- Electronic in 2006 and his MS and Ph.D. degrees in biomedical engineering-bioelectric in 2008 and 2015, respectively, from Islamic Azad University, Science and Research Branch, Tehran, Iran. Now he is an assistant professor in the Biomedical Engineering Department at Tehran Medical Science, Islamic Azad University, Tehran, Iran. His current research interests are in the field of biomedical signal and image processing, nonlinear time series analysis, and cognitive science. Particular applications include ECG, HRV, and EEG Signal Processing in detection and prediction of diseases, Epileptic Seizure Prediction, pattern recognition and Image Processing for face and beauty recognition, watermarking and etc. Zahra Khandaghi, received the B.S. degree in biomedical engineering from Tehran Medical Science, Islamic Azad University, Tehran, Iran and currently she’s a graduate student in medical radiation engineering from Shahid Beheshti University, Tehran, Iran. Her research has focused on the design part of Hardware MRI system. Her research interests include image processing, analysis of biomedical signals for the detection of disease. Mahsa Shahipour, received the B.S. degree in biomedical engineering from Islamic Azad University of Tehran Medical Branch and currently she's graduate student in Bioelectric Medical Engineering from Islamic Azad University, North Tehran Branch, Tehran, Iran. Her research interests include signal and image processing.