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Journals / Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi / 2019 / Cilt: 6 - Sayı: 1

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

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

İdil IŞIKLI ESENER
Pages
12–23
Publication Date
2019-06-28
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

These outcomes clearly show that the proposed feature set can confidently be integrated into the driving assistance systems.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This paper proposes a novel feature set for drivers’ stress level recognition.
📊
Key Findings & Data: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.
💡
Conclusion & Impact:These outcomes clearly show that the proposed feature set can confidently be integrated into the driving assistance systems.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Stress recognition · Feature selection · Feature correlation

Abstract

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.

Özet

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.

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Cite This Article

ESENER, İ. I. (2019). A Novel Stress-Level-Specific Feature Ensemble for Drivers’ Stress Level Recognition. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, Cilt: 6 - Sayı: 1, 12–23. https://ajindex.com/articles/251614

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References (15)

  1. Selye, H. (1976). Stress without distress. Psychopathology of Human Adaptation Serban G. (Eds.). Springer, Boston, MA, 137-146.
  2. 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.
  3. 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.
  4. Simon, F. & Corbett, C. (1996) Road traffic offending, stress, age, and accident history among male and female drivers. Ergonomics, 39 (5), 757–780.
  5. 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.
  6. 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.
  7. 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
  8. June, Ajaccio, France, 627–632.
  9. Healey, J. & Picard, R. (2002). SmartCar: Detecting Driver Stress. 15. International Conference on Pattern Recognition, 3-7
  10. September, Barcelona, Spain, 4, 218–221.
  11. 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.
  12. Akbaş, A. (2011). Evaluation of the physiological data indicating the dynamic stress level of drivers, Scientific Research and Essays, 6 (2), 430-439.
  13. 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.
  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.
  15. Deng, Y., Wu, Z., Chu, C. H., & Yang, T. (2012). Evaluating Feature Selection for Stress Identification. IEEE