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A Novel Stress-Level-Specific Feature Ensemble for Drivers’ Stress Level Recognition

Yıl 2019, Cilt: 6 Sayı: 1, 12 - 23, 28.06.2019

İdil Işıklı Esener

https://doi.org/10.35193/bseufbd.554791

Öz

This
paper proposes a novel feature set for drivers’ stress level recognition. The
proposed feature set consists of data-independent and almost uncorrelated feature
pairs for each stress level with very strong intra-class and relatively weak
inter-class correlations, constructed by realizing a correlation analysis on
the popular features studied in the literature. By using the proposed feature
set, a maximum of 100% stress level recognition accuracy is achieved with an
average increment of 24.85% while a mean reduction rate of 88.01% is satisfied
in false positive rate compared to the full feature set. These outcomes clearly
show that the proposed feature set can confidently be integrated into the
driving assistance systems.

Anahtar Kelimeler

Stress recognition Feature selection Feature correlation

Kaynakça

  • Selye, H. (1976). Stress without distress. Psychopathology of Human Adaptation Serban G. (Eds.). Springer, Boston, MA, 137-146.
  • Rastgoo, M. N., Nakisa, B., Rakotonirainy, A., Chandran, V., & Tjondronegoro, D. (2018). A critical review of proactive detection of driver stress levels based on multimodal measurements. ACM Computing Surveys, 51, 1–35.
  • Beirness, D. J. (1993). Do we really drive as we live? The role of personality factors in road crashes. Alcohol, Drugs, and Driving: Abstracts and Reviews, 9 (3), 129-143.
  • Simon, F. & Corbett, C. (1996) Road traffic offending, stress, age, and accident history among male and female drivers. Ergonomics, 39 (5), 757–780.
  • Miller, L. H., Smith, A. D., & Rothstein, L. (1994). The Stress Solution: An Action Plan to Manage the Stress in Your Life reprint ed., Pocket Books, New York.
  • Rodrigues, J. G. P., Kaiseler, M., Aguiar, A., Cunha, J. P. S., & Barros, J. (2015). A mobile sensing approach to stress detection and memory activation for public bus drivers. IEEE Transactions on Intelligent Transportation Systems, 16, 3294–3303.
  • Katsis, C. D., Katertsidis, N., Ganiatsas, G., & Fotiadis, D. I. (2008). Toward emotion recognition in car-racing drivers: A biosignal processing approach, IEEE Transactions on Systems, Man, and Cybernetics - Part A. 38 (3), 502–512.
  • Rigas, G., Katsis, C. D., Bougia, P., & Fotiadis, D. I. (2008). A Reasoning-Based Framework for Car Driver’s Stress Prediction. 16. Mediterranean Conference on Control and Automation, 25-27 June, Ajaccio, France, 627–632.
  • Healey, J. & Picard, R. (2002). SmartCar: Detecting Driver Stress. 15. International Conference on Pattern Recognition, 3-7 September, Barcelona, Spain, 4, 218–221.
  • Healey, J. A. & Picard, R. W. (2005). Detecting stress during real-world driving tasks using physiological sensors, IEEE Transactions on Intelligent Transportation Systems, 6 (2), 156–166.
  • Akbaş, A. (2011). Evaluation of the physiological data indicating the dynamic stress level of drivers, Scientific Research and Essays, 6 (2), 430-439.
  • Rigas, G., Goletsis, Y., Bougias, P., & Fotiadis, D. I. (2011). Towards driver’s state recognition on real driving conditions. International Journal of Vehicular Technology, 2011, 1-14.
  • Rigas, G., Goletsis, Y., & Fotiadis, D. (2012). Real-time driver’s stress event detection. IEEE Transactions on Intelligent Transportation Systems, 13 (1), 221–234.
  • Deng, Y., Wu, Z., Chu, C. H., & Yang, T. (2012). Evaluating Feature Selection for Stress Identification. IEEE 13. International Conference on Information Reuse & Integration, 8-10 August, Las Vegas, NV, USA, 584–591.
  • Soman, K., Alex, V., & Srinivas, C. (2013). Analysis of Physiological Signals in Response to Stress using ECG and Respiratory Signals of Automobile Drivers. 2013 International Mutli-Conference on Automation, Computing, Communication, Control and Compressed, 22-23 March, Kottayam, Kerala, India, 574-579.
  • Singh, M. & Bin Queyam, A. (2013). A novel method of stress detection using physiological measurements of automobile drivers. International Journal of Electronics Engineering, 5 (2), 13–20.
  • Deng, Y., Wu, Z., Chu, C. H., Zhang, Q., & Hsu, D. F. (2013). Sensor feature selection and combination for stress identification using combinatorial fusion. International Journal of Advanced Robotic Systems, 10, 306-313.
  • Wang, J. S., Lin, C. W., & Yang, Y. T. C. (2013). A k-nearest-neighbor classifier with heart rate variability feature-based transformation algorithm for driving stress recognition. Neurocomputing. 116, 136–143.
  • Singh, R. R., Conjeti, S., & Banerjee, R. (2013). A comparative evaluation of neural network classifiers for stress level analysis of automotive drivers using physiological signals, Biomedical Signal Processing and Control, 8 (6), 740-754.
  • Avcı C., Akbaş, A., & Yüksel, Y. (2014). Evaluation of Statistical Metrics by using Physiological Data to Identify the Stress Level of Drivers. 3. International Conference on Environment, Chemistry and Biology, 29-30 November, Port Louis, Mauritius, 124-128.
  • Soman, K., Sathiya, A., & Suganthi, N. (2015). Classification of Stress of Automobile Drivers using Radial Basis Function Kernel Support Vector Machine. 2014 IEEE International Conference on Information Communication & Embedded Systems, 27-28 February, Chennai, India, 1-5.
  • Keshan, N., Parimi, P.V., & Bichindaritz, I. (2015). Machine Learning for Stress Detection from ECG Signals in Automobile Drivers. 2015 IEEE International Conference on Big Data, 29 October-1 November, Santa Clara, CA, USA, 2661–2669.
  • Lanatà, A., Valenza, G., Greco, A., Gentili, C., Bartolozzi, R, Bucchi, F., Frendo, F, & Scilingo, E. P. (2015). How the autonomic nervous system and driving style change with incremental stressing conditions during simulated driving. IEEE Transactions on Intelligent Transportation Systems, 16 (3), 1505-1517.
  • Heikoop, D. D., de Winter, J. C. F., Arem, B, & Stanton, N. A. (2016). Effects of platooning on signal-detection performance, workload, and stress: A driving simulator study. Applied Ergonomics, 60, 116-127.
  • Chen, L., Zhao, Y., Ye, P., Zhang, J., & Zou, J. (2017). Detecting driving stress in physiological signals based on multimodal feature analysis and kernel classifiers. Expert System with Applications, 85, 279-291.
  • Ollander, S., Godin, C., Charbonnier, S., & Campagne, A. (2016). Feature and Sensor Selection for Detection of Driver Stress. 3. International Conference on Physiological Computing Systems, 27-28 July, Lisbon, Portugal, 115–122.
  • Urbano, M., Alam, M., Ferreira, J., Fonseca , J., & Simíões, P. (2017). Cooperative Driver Stress Sensing İntegration with Ecall System for Improved Road Safety. 17. International Conference on Smart Technologies, 6-8 July, Ohrid, Macedonia, 883-888.
  • Zheng, R., Yamabe, S., Nakano, K., & Suda, Y. (2015). Biosignal analysis to assess mental stress in automatic driving of trucks: palmar perspiration and masseter electromyography. Sensors, 15, 5136-5150.
  • Goldberger A. L., Amaral L. A. N., Glass L., Hausdorff J. M., Ivanov P. Ch., Mark R. G., Mietus J. E., Moody G. B., Peng C-K., & Stanley H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 101 (23), 215-220.
  • Deshmukh, S. V. (2018). Study of online driver distraction analysis using ECG-dynamics. Master of Science Thesis, University of Michigan, Computer and Information Sciences, Dearborn, Michigan.

Sürücü Stres Seviyesi Tanıma için Stres-Seviyesine-Özgü Yeni Bir Öznitelik Topluluğu

Yıl 2019, Cilt: 6 Sayı: 1, 12 - 23, 28.06.2019

İdil Işıklı Esener

https://doi.org/10.35193/bseufbd.554791

Öz

This
paper proposes a novel feature set for drivers’ stress level recognition. The
proposed feature set consists of data-independent and almost uncorrelated
feature pairs for each stress level with very strong intra-class and relatively
weak inter-class correlations, constructed by realizing a correlation analysis
on the popular features studied in the literature. By using the proposed
feature set, a maximum of 100% stress level recognition accuracy is achieved
with an average increment of 24.85% while a mean reduction rate of 88.01% is satisfied
in false positive rate compared to the full feature set. These outcomes clearly
show that the proposed feature set can confidently be integrated into the
driving assistance systems.

Anahtar Kelimeler

Stress Recognition Feature Selection Feature Correlation

Kaynakça

  • Selye, H. (1976). Stress without distress. Psychopathology of Human Adaptation Serban G. (Eds.). Springer, Boston, MA, 137-146.
  • Rastgoo, M. N., Nakisa, B., Rakotonirainy, A., Chandran, V., & Tjondronegoro, D. (2018). A critical review of proactive detection of driver stress levels based on multimodal measurements. ACM Computing Surveys, 51, 1–35.
  • Beirness, D. J. (1993). Do we really drive as we live? The role of personality factors in road crashes. Alcohol, Drugs, and Driving: Abstracts and Reviews, 9 (3), 129-143.
  • Simon, F. & Corbett, C. (1996) Road traffic offending, stress, age, and accident history among male and female drivers. Ergonomics, 39 (5), 757–780.
  • Miller, L. H., Smith, A. D., & Rothstein, L. (1994). The Stress Solution: An Action Plan to Manage the Stress in Your Life reprint ed., Pocket Books, New York.
  • Rodrigues, J. G. P., Kaiseler, M., Aguiar, A., Cunha, J. P. S., & Barros, J. (2015). A mobile sensing approach to stress detection and memory activation for public bus drivers. IEEE Transactions on Intelligent Transportation Systems, 16, 3294–3303.
  • Katsis, C. D., Katertsidis, N., Ganiatsas, G., & Fotiadis, D. I. (2008). Toward emotion recognition in car-racing drivers: A biosignal processing approach, IEEE Transactions on Systems, Man, and Cybernetics - Part A. 38 (3), 502–512.
  • Rigas, G., Katsis, C. D., Bougia, P., & Fotiadis, D. I. (2008). A Reasoning-Based Framework for Car Driver’s Stress Prediction. 16. Mediterranean Conference on Control and Automation, 25-27 June, Ajaccio, France, 627–632.
  • Healey, J. & Picard, R. (2002). SmartCar: Detecting Driver Stress. 15. International Conference on Pattern Recognition, 3-7 September, Barcelona, Spain, 4, 218–221.
  • Healey, J. A. & Picard, R. W. (2005). Detecting stress during real-world driving tasks using physiological sensors, IEEE Transactions on Intelligent Transportation Systems, 6 (2), 156–166.
  • Akbaş, A. (2011). Evaluation of the physiological data indicating the dynamic stress level of drivers, Scientific Research and Essays, 6 (2), 430-439.
  • Rigas, G., Goletsis, Y., Bougias, P., & Fotiadis, D. I. (2011). Towards driver’s state recognition on real driving conditions. International Journal of Vehicular Technology, 2011, 1-14.
  • Rigas, G., Goletsis, Y., & Fotiadis, D. (2012). Real-time driver’s stress event detection. IEEE Transactions on Intelligent Transportation Systems, 13 (1), 221–234.
  • Deng, Y., Wu, Z., Chu, C. H., & Yang, T. (2012). Evaluating Feature Selection for Stress Identification. IEEE 13. International Conference on Information Reuse & Integration, 8-10 August, Las Vegas, NV, USA, 584–591.
  • Soman, K., Alex, V., & Srinivas, C. (2013). Analysis of Physiological Signals in Response to Stress using ECG and Respiratory Signals of Automobile Drivers. 2013 International Mutli-Conference on Automation, Computing, Communication, Control and Compressed, 22-23 March, Kottayam, Kerala, India, 574-579.
  • Singh, M. & Bin Queyam, A. (2013). A novel method of stress detection using physiological measurements of automobile drivers. International Journal of Electronics Engineering, 5 (2), 13–20.
  • Deng, Y., Wu, Z., Chu, C. H., Zhang, Q., & Hsu, D. F. (2013). Sensor feature selection and combination for stress identification using combinatorial fusion. International Journal of Advanced Robotic Systems, 10, 306-313.
  • Wang, J. S., Lin, C. W., & Yang, Y. T. C. (2013). A k-nearest-neighbor classifier with heart rate variability feature-based transformation algorithm for driving stress recognition. Neurocomputing. 116, 136–143.
  • Singh, R. R., Conjeti, S., & Banerjee, R. (2013). A comparative evaluation of neural network classifiers for stress level analysis of automotive drivers using physiological signals, Biomedical Signal Processing and Control, 8 (6), 740-754.
  • Avcı C., Akbaş, A., & Yüksel, Y. (2014). Evaluation of Statistical Metrics by using Physiological Data to Identify the Stress Level of Drivers. 3. International Conference on Environment, Chemistry and Biology, 29-30 November, Port Louis, Mauritius, 124-128.
  • Soman, K., Sathiya, A., & Suganthi, N. (2015). Classification of Stress of Automobile Drivers using Radial Basis Function Kernel Support Vector Machine. 2014 IEEE International Conference on Information Communication & Embedded Systems, 27-28 February, Chennai, India, 1-5.
  • Keshan, N., Parimi, P.V., & Bichindaritz, I. (2015). Machine Learning for Stress Detection from ECG Signals in Automobile Drivers. 2015 IEEE International Conference on Big Data, 29 October-1 November, Santa Clara, CA, USA, 2661–2669.
  • Lanatà, A., Valenza, G., Greco, A., Gentili, C., Bartolozzi, R, Bucchi, F., Frendo, F, & Scilingo, E. P. (2015). How the autonomic nervous system and driving style change with incremental stressing conditions during simulated driving. IEEE Transactions on Intelligent Transportation Systems, 16 (3), 1505-1517.
  • Heikoop, D. D., de Winter, J. C. F., Arem, B, & Stanton, N. A. (2016). Effects of platooning on signal-detection performance, workload, and stress: A driving simulator study. Applied Ergonomics, 60, 116-127.
  • Chen, L., Zhao, Y., Ye, P., Zhang, J., & Zou, J. (2017). Detecting driving stress in physiological signals based on multimodal feature analysis and kernel classifiers. Expert System with Applications, 85, 279-291.
  • Ollander, S., Godin, C., Charbonnier, S., & Campagne, A. (2016). Feature and Sensor Selection for Detection of Driver Stress. 3. International Conference on Physiological Computing Systems, 27-28 July, Lisbon, Portugal, 115–122.
  • Urbano, M., Alam, M., Ferreira, J., Fonseca , J., & Simíões, P. (2017). Cooperative Driver Stress Sensing İntegration with Ecall System for Improved Road Safety. 17. International Conference on Smart Technologies, 6-8 July, Ohrid, Macedonia, 883-888.
  • Zheng, R., Yamabe, S., Nakano, K., & Suda, Y. (2015). Biosignal analysis to assess mental stress in automatic driving of trucks: palmar perspiration and masseter electromyography. Sensors, 15, 5136-5150.
  • Goldberger A. L., Amaral L. A. N., Glass L., Hausdorff J. M., Ivanov P. Ch., Mark R. G., Mietus J. E., Moody G. B., Peng C-K., & Stanley H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 101 (23), 215-220.
  • Deshmukh, S. V. (2018). Study of online driver distraction analysis using ECG-dynamics. Master of Science Thesis, University of Michigan, Computer and Information Sciences, Dearborn, Michigan.
Toplam 30 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

İdil Işıklı Esener

Yayımlanma Tarihi 28 Haziran 2019
Gönderilme Tarihi 17 Nisan 2019
Kabul Tarihi 3 Mayıs 2019
Yayımlandığı Sayı Yıl 2019 Cilt: 6 Sayı: 1

Kaynak Göster

APA Işıklı Esener, İ. (2019). A Novel Stress-Level-Specific Feature Ensemble for Drivers’ Stress Level Recognition. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, 6(1), 12-23. https://doi.org/10.35193/bseufbd.554791