Smart car system: automobile driver's stress recognition with artificial neural networks
الموضوعات : فصلنامه شبیه سازی و تحلیل تکنولوژی های نوین در مهندسی مکانیک
Mahtab Vaezi
1
,
Mehdi Nasri
2
,
Farhad Azimifar
3
1 - Department of Biomedical Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
2 - Electrical Engineering department, Khomeinishahr branch, Islamic Azad University, Isfahan, Iran
3 - Department of Biomedical Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
الکلمات المفتاحية: Optimization, Neural Networks, smart machine, stress recognition, Relief feature selection,
ملخص المقالة :
Nowadays, the world needs safe and smart machines that can prevent human errors in different situations. Stress is an important factor in accidents which causes the human error. Many accidents can be prevented by identifying the stress of the driver and warning them. Due to its complexity, identifying stress in drivers is only possible by intelligent algorithms. In this paper, the Electrocardiogram (ECG) signal from drivedb dataset is used to detect stress in drivers, which has useful information that can be recorded more easily while driving. Afterwards, with a set of statistical, entropy, morphology, and chaos features, useful information is extracted from the signal. Then, in order to optimize the features, the Relief feature selector is used. Optimal features information is evaluated using Artificial Neural Networks (ANNs). Using the proposed method, the stress in drivers is detected with an accuracy of 92.6%, which has increased classification accuracy compared to recent researches.
[1] W. Hafez, "Human digital twin: Enabling human-multi smart machines collaboration," in Proceedings of SAI Intelligent Systems Conference, 2019, pp. 981-993: Springer.
[2] H. Selye, "Stress without distress," in Psychopathology of human adaptation: Springer, 1976, pp. 137-146.
[3] T. G. Brown et al., "Personality, executive control, and neurobiological characteristics associated with different forms of risky driving," PLoS one, vol. 11, no. 2, p. e0150227, 2016.
[4] S. A. Useche, V. G. Ortiz, and B. E. Cendales, "Stress-related psychosocial factors at work, fatigue, and risky driving behavior in bus rapid transport (BRT) drivers," Accident Analysis Prevention, vol. 104, pp. 106-114, 2017.
[5] N. Keshan, P. Parimi, and I. Bichindaritz, "Machine learning for stress detection from ECG signals in automobile drivers," in 2015 IEEE International conference on big data (Big Data), 2015, pp. 2661-2669: IEEE.
[6] L. Mou et al., "Driver stress detection via multimodal fusion using attention-based CNN-LSTM," Expert Systems with Applications, vol. 173, p. 114693, 2021.
[7] G. Yan, M. Wang, P. Qin, T. Yan, Y. Bao, and X. Wang, "Comparative study on drivers’ eye movement characteristics and psycho-physiological reactions at tunnel entrances in plain and high-altitude areas: A pilot study," Tunnelling Underground Space Technology, vol. 122, p. 104370, 2022.
[8] M. Pedrotti et al., "Automatic stress classification with pupil diameter analysis," International Journal of Human-Computer Interaction, vol. 30, no. 3, pp. 220-236, 2014.
[9] H. Gao, A. Yüce, and J.-P. Thiran, "Detecting emotional stress from facial expressions for driving safety," in 2014 IEEE International Conference on Image Processing (ICIP), 2014, pp. 5961-5965: IEEE.
[10] O. V. Bitkina, J. Kim, J. Park, J. Park, and H. K. Kim, "Identifying traffic context using driving stress: A longitudinal preliminary case study," Sensors, vol. 19, no. 9, p. 2152, 2019.
[11] H. Yokoyama, K. Eihata, J. Muramatsu, and Y. Fujiwara, "Prediction of Driver's Workload from Slow Fluctuations of Pupil Diameter," in 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 2018, pp. 1775-1780: IEEE.
[12] C. Schießl, "Stress and strain while driving," in Proceedings of the Young Researchers Seminar-European Conference of Transport Research Institutes, Brno, Czech Republic, 2007, pp. 27-30.
[13] H. Jebelli, M. M. Khalili, and S. Lee, "Mobile EEG-based workers’ stress recognition by applying deep neural network," in Advances in informatics and computing in civil and construction engineering: Springer, 2019, pp. 173-180.
[14] R. Vaitheeshwari, S.-C. Yeh, E. H.-K. Wu, J.-Y. Chen, and C.-R. Chung, "Stress Recognition Based on Multi-Physiological Data in High Pressure Driving VR Scene," IEEE Sensors Journal, 2022.
[15] P. Zontone, A. Affanni, A. Piras, and R. Rinaldo, "Stress recognition in a simulated city environment using Skin Potential Response (SPR) signals," in 2021 IEEE International Workshop on Metrology for Automotive (MetroAutomotive), 2021, pp. 135-140: IEEE.
[16] P. Zontone, A. Affanni, R. Bernardini, L. Del Linz, A. Piras, and R. Rinaldo, "Analysis of Physiological Signals for Stress Recognition with Different Car Handling Setups," Electronics, vol. 11, no. 6, p. 888, 2022.
[17] W. e. Hadi, N. El-Khalili, M. AlNashashibi, G. Issa, and A. A. AlBanna, "Application of data mining algorithms for improving stress prediction of automobile drivers: A case study in Jordan," Computers in biology medicine, vol. 114, p. 103474, 2019.
[18] I. C. Jeong, D. H. Lee, S. W. Park, J. I. Ko, and H. R. Yoon, "Automobile driver's stress index provision system that utilizes electrocardiogram," in 2007 IEEE Intelligent Vehicles Symposium, 2007, pp. 652-656: IEEE.
[19] G. C. Butler, Y. Yamamoto, and R. L. Hughson, "Heart rate variability to monitor autonomic nervous system activity during orthostatic stress," The Journal of Clinical Pharmacology, vol. 34, no. 6, pp. 558-562, 1994.
[20] M. Vaezi and M. Nasri, Sleep Scoring Based on Biomedical Signals: A Survey and a New Algorithm (Advances in Signal Processing, Book Series, Vol. 2). IFSA Publishing, 2021.
[21] J. A. Healey and R. W. Picard, "Detecting stress during real-world driving tasks using physiological sensors," IEEE Transactions on intelligent transportation systems, vol. 6, no. 2, pp. 156-166, 2005.
[22] P. O. Seglen, "The skewness of science," Journal of the American society for information science
vol. 43, no. 9, pp. 628-638, 1992.
[23] K. P. Balanda and H. MacGillivray, "Kurtosis: a critical review," The American Statistician, vol. 42, no. 2, pp. 111-119, 1988.
[24] A. J. Camm et al., "Heart rate variability. Standards of measurement, physiological interpretation, and clinical use," 1996.
[25] J. L. Lebowitz, "Boltzmann's entropy and time's arrow," Physics today, vol. 46, pp. 32-32, 1993.
[26] A. P. Guerrero and G. E. Paredes, Linear and non-linear stability analysis in boiling water reactors: the design of real-time stability monitors. Woodhead Publishing, 2018.
[27] V. Latora and M. Baranger, "Kolmogorov-Sinai entropy rate versus physical entropy," Physical Review Letters, vol. 82, no. 3, p. 520, 1999.
[28] P. Memar and F. Faradji, "A novel multi-class EEG-based sleep stage classification system," IEEE Transactions on Neural Systems Rehabilitation Engineering, vol. 26, no. 1, pp. 84-95, 2017.
[29] G. Boeing, "Visual analysis of nonlinear dynamical systems: chaos, fractals, self-similarity and the limits of prediction," Systems, vol. 4, no. 4, p. 37, 2016.
[30] M. Vaezi and M. Nasri, "Application of Heuristic Feature Selection in EEG based Sleep Stages Classification," in 2020 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), 2020, pp. 1-6: IEEE.
[31] M. Vaezi and M. Nasri, "Sleep Stage Classification using Laplacian Score Feature Selection Method by Single Channel EEG," Majlesi Journal of Electrical Engineering, vol. 14, no. 4, pp. 11-19, 2020.
[32] Y.-H. Chen and S.-N. Yu, "Selection of effective features for ECG beat recognition based on nonlinear correlations," Artificial intelligence in medicine, vol. 54, no. 1, pp. 43-52, 2012.
[33] J. Yang and R. Yan, "A multidimensional feature extraction and selection method for ECG arrhythmias classification," IEEE Sensors Journal, vol. 21, no. 13, pp. 14180-14190, 2020.
[34] A. Elsayyad, M. Al-Dhaifallah, and A. M. Nassef, "Features selection for arrhythmia diagnosis using Relief-F algorithm and support vector machine," in 2017 14th International Multi-Conference on Systems, Signals & Devices (SSD), 2017, pp. 461-468: IEEE.
[35] K. Kira and L. A. Rendell, "The feature selection problem: Traditional methods and a new algorithm," in Aaai, 1992, vol. 2, no. 1992a, pp. 129-134.
[36] I. Kononenko, E. Šimec, and M. Robnik-Šikonja, "Overcoming the myopia of inductive learning algorithms with RELIEFF," Applied Intelligence, vol. 7, no. 1, pp. 39-55, 1997.
[37] P. Ghosh et al., "Efficient prediction of cardiovascular disease using machine learning algorithms with relief and LASSO feature selection techniques," IEEE Access, vol. 9, pp. 19304-19326, 2021.
[38] R. J. Urbanowicz, M. Meeker, W. La Cava, R. S. Olson, and J. H. Moore, "Relief-based feature selection: Introduction and review," Journal of biomedical informatics, vol. 85, pp. 189-203, 2018.
[39] K. Potdar, T. S. Pardawala, and C. D. Pai, "A comparative study of categorical variable encoding techniques for neural network classifiers," International journal of computer applications, vol. 175, no. 4, pp. 7-9, 2017.
[40] B. Lantz, Machine learning with R: expert techniques for predictive modeling. Packt publishing ltd, 2019.
[41] M. Vaezi and M. Nasri, "AS3-SAE: Automatic Sleep Stages Scoring using Stacked Autoencoders," Frontiers in Biomedical Technologies, 2022.
[42] T.-T. Wong, "Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation," Pattern Recognition
vol. 48, no. 9, pp. 2839-2846, 2015.
[43] J.-S. Wang, C.-W. Lin, and Y.-T. C. Yang, "A k-nearest-neighbor classifier with heart rate variability feature-based transformation algorithm for driving stress recognition," Neurocomputing, vol. 116, pp. 136-143, 2013.
[44] K. T. Chui, K. F. Tsang, H. R. Chi, C. K. Wu, and B. W.-K. Ling, "Electrocardiogram based classifier for driver drowsiness detection," in 2015 IEEE 13th International Conference on Industrial Informatics (INDIN), 2015, pp. 600-603: IEEE.
[45] K. T. Chui, M. D. Lytras, and R. W. Liu, "A generic design of driver drowsiness and stress recognition using MOGA optimized deep MKL-SVM," Sensors, vol. 20, no. 5, p. 1474, 2020.
