Abstract
Driver fatigue is one of the main causes of road traffic accidents. Because of the individual differences between drivers, the common characteristics of driver fatigue are not clear, and existing fatigue identification algorithms cannot meet the prospective requirements of fatigue identification. In this study, based on a driver's electrocardiogram signal, 17 feature indices were extracted as candidate features for driver fatigue classification from three aspects: frequency domain features, time domain features and non-linear features. In order to improve the generalization ability of the driver fatigue classification and recognition model and to reduce the negative impact of individual variability on the generalized model, the support vector machine (SVM) discriminant function information was introduced as the ranking evaluation criterion, the recursive feature elimination (SVM-RFE) algorithm was used as the search strategy, and the correlation bias reduction algorithm was introduced to avoid the evaluation bias, and the importance ranking of the fatigue feature indicators was realized. A ranked list of the importance of the candidate features in the fatigue sample of each test driver was generated. Based on the ranked list of features for each test driver, an evaluation matrix K of driver fatigue feature importance was proposed, and the feature importance index Va was defined. To quantify the importance of candidate features for overall fatigue identification, eight key features that significantly characterize driver fatigue were selected as the optimal feature subset. Finally, a two- class identification model of the driver fatigue state based on the SVM algorithm was designed to verify the effectiveness of the identification model considering individual differences. The results show that the average classification accuracy of the proposed identification model was 87.04%, which was 9.20% higher than that of a model that did not consider individual driver differences. The performance of the model was significantly improved by selecting features that were less affected by individual differences in the driver fatigue identification model.
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References
Alam L, Hoque MM (2018) Vision-based driver’s attention monitoring system for smart vehicles. International Conference on Intelligent Computing & Optimization. Springer, Cham, pp. 196–209
Balandong RP, Ahmad RF, Saad MNM et al (2018) A review on EEG-based automatic sleepiness detection systems for driver. IEEE Access 6:22908–22919
Balasubramanian V, Adalarasu K (2007) EMG-based analysis of change in muscle activity during simulated driving. J Bodyw Mov Ther 11(2):151–158
Casal R, Di Persia LE, Schlotthauer G (2019) Sleep-wake stages classification using heart rate signals from pulse oximetry. Heliyon 5(10):e02529
Chowdhury A, Shankaran R, Kavakli M et al (2018) Sensor applications and physiological features in drivers’ drowsiness detection: a review. IEEE Sens J 18(8):3055–3067
Chu W, Wu C, Zhang H, Yang M, Li S (2018) A personalized behavior model-based approach to driver fatigue identification. Chin J Safe Sci 28(06):43–48
Del Rio-Bermudez C, Diaz-Piedra C, Catena A et al (2014) Chronotype-dependent circadian rhythmicity of driving safety. Chronobiol Int 31(4):532–541
Du G, Zhang L, Su K et al (2022) A multimodal fusion fatigue driving detection method based on heart rate and PERCLOS. IEEE Trans Intell Transp Syst 23(11):21810–21820
Khan MQ, Lee S (2019) A comprehensive survey of driving monitoring and assistance systems. Sensors 19(11):2574
Kumar SP, Murugan S, Selvaraj J et al (2021) Detecting driver mental fatigue based on Electroencephalogram (EEG) signals during simulated driving. IOP Conf Ser Mater Sci Eng IOP Publishing 1070(1):012096
Lal SKL, Craig A (2001) A critical review of the psychophysiology of driver fatigue. Biol Psychol 55(3):173–194
Lawoyin S, Fei DY, Bai O (2015) Accelerometer-based steering-wheel movement monitoring for drowsy-driving detection. Proceedings of the Institution of Mechanical Engineers Part D. J Automob Eng 229(2):163–173
Li Z, Chen L, Nie L, Yang SX (2021) A novel learning model of driver fatigue features representation for steering wheel angle. IEEE Trans Veh Technol 71(1):269–281. https://doi.org/10.1109/tvt.2021.3130152
Liu CC, Hosking SG, Lenné MG (2009) Predicting driver drowsiness using vehicle measures: recent insights and future challenges. J Safety Res 40(4):239–245
Motin MA, Kamakar C, Marimuthu P, Penzel T (2020) Photoplethysmographic-based automated sleep-wake classification using a support vector machine. Physiol Meas 41(7):075013. https://doi.org/10.1088/1361-6579/ab9482
Raman KJ, Azman A, Arumugam V, et al. (2018) Fatigue monitoring based on yawning and head movement. 6th International Conference on Information and Communication Technology (ICoICT). IEEE 2018: 343–347
Sooksatra S, Kondo T, Bunnun PA (2015) Robust method for drowsiness detection using distance and gradient vectors. 12th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE 2015:1–5
Wang X, Xu C (2016) Driver drowsiness detection based on non-intrusive metrics considering individual specifics. Accid Anal Prev 95:350–357
Xu C, Pei S, Wang X (2016) Individual differentiated driving fatigue detection based on non-invasive measurement index. China J Highw Transp 29(10):118–125
Yan K, Zhang D (2015) Feature selection and analysis on correlated gas sensor data with recursive feature elimination. Sens Actuators, B Chem 212:353–363
Yang G, Lin Y, Bhattacharya P (2010) A driver fatigue recognition model based on information fusion and dynamic Bayesian network. Inf Sci 180(10):1942–1954
You F et al (2019) A real-time driving drowsiness detection algorithm with individual differences consideration. IEEE Access 7:179396–179408
Zhang X, Wang X, Yang X et al (2020) Driver drowsiness detection using mixed-effect ordered logit model considering time cumulative effect. Anal Methods Accident Res 26:100114
Funding
The project was funded by National Key R&D Program of China (2021YFC3001500), Youth Program of National Natural Science Foundation of China (52002143) and the Transportation Innovation and Development Support Project of Jilin Province (2023-1-12).
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Wencai Sun: Conceptualization, Methodology, Project administration, Writing- Review &Editing. Wei Jiang: Data curation, Software, Formal analysis, Methodology. Yihao Si: Software, Visualization, Investigation. Chen Li, Shiwu Li: Writing-Review &Editing, Supervision. Chen Li, Mengzhu Guo, Dezhi Liu, Huijun Song: Writing – Original Draft, Validation.
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Sun, W., Jiang, W., Li, C. et al. Study on identification method of driver fatigue considering individual ECG differences. Cogn Tech Work 26, 301–312 (2024). https://doi.org/10.1007/s10111-024-00755-9
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DOI: https://doi.org/10.1007/s10111-024-00755-9