Feature Engineering and Machine Learning for Driver Sleepiness Detection
暂无分享,去创建一个
[1] G. Kecklund,et al. Sleepiness in long distance truck driving: an ambulatory EEG study of night driving. , 1993, Ergonomics.
[2] Nathaniel H. Hunt,et al. The Appropriate Use of Approximate Entropy and Sample Entropy with Short Data Sets , 2012, Annals of Biomedical Engineering.
[3] J. Bergeron,et al. Fatigue and individual differences in monotonous simulated driving , 2003 .
[4] Wendy Macdonald,et al. Review of Relationships Between Steering Wheel Reversal Rate and Driving Task Demand , 1980 .
[5] Göran Kecklund,et al. Predicting road crashes from a mathematical model of alertness regulation--The Sleep/Wake Predictor. , 2008, Accident; analysis and prevention.
[6] P. Laguna,et al. Detection of driver's drowsiness by means of HRV analysis , 2011, 2011 Computing in Cardiology.
[7] T. Higuchi. Approach to an irregular time series on the basis of the fractal theory , 1988 .
[8] T. Åkerstedt,et al. Subjective and objective sleepiness in the active individual. , 1990, The International journal of neuroscience.
[9] Michael Schrauf,et al. EEG alpha spindle measures as indicators of driver fatigue under real traffic conditions , 2011, Clinical Neurophysiology.
[10] A. Wayne Whitney,et al. A Direct Method of Nonparametric Measurement Selection , 1971, IEEE Transactions on Computers.
[11] D. Abásolo,et al. Use of the Higuchi's fractal dimension for the analysis of MEG recordings from Alzheimer's disease patients. , 2009, Medical Engineering and Physics.
[12] T. Åkerstedt,et al. Use of subjective and physiological indicators of sleepiness to predict performance during a vigilance task. , 2007, Industrial health.
[13] Madalena Costa,et al. Multiscale entropy analysis of biological signals. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.
[14] Peter Rossiter,et al. Applying neural network analysis on heart rate variability data to assess driver fatigue , 2011, Expert Syst. Appl..
[15] Andry Rakotonirainy,et al. Driving performance impairments due to hypovigilance on monotonous roads. , 2011, Accident; analysis and prevention.
[16] Joshua S. Martin,et al. Removing the entropy from the definition of entropy: clarifying the relationship between evolution, entropy, and the second law of thermodynamics , 2013, Evolution: Education and Outreach.
[17] Prabir Bhattacharya,et al. A driver fatigue recognition model based on information fusion and dynamic Bayesian network , 2010, Inf. Sci..
[18] Rongrong Fu,et al. Automated Detection of Driver Fatigue Based on Entropy and Complexity Measures , 2014, IEEE Transactions on Intelligent Transportation Systems.
[19] A. Malliani,et al. Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .
[20] R. Uthayakumar,et al. Application of fractal theory in analysis of human electroencephalographic signals , 2008, Comput. Biol. Medicine.
[21] D. Dinges,et al. The cumulative cost of additional wakefulness: dose-response effects on neurobehavioral functions and sleep physiology from chronic sleep restriction and total sleep deprivation. , 2003, Sleep.
[22] C. Apte,et al. Data mining with decision trees and decision rules , 1997, Future Gener. Comput. Syst..
[23] Jirí Mekyska,et al. Assessing progress of Parkinson's disease using acoustic analysis of phonation , 2015, 2015 4th International Work Conference on Bioinspired Intelligence (IWOBI).
[24] Bruno Jammes,et al. Automatic EOG analysis: A first step toward automatic drowsiness scoring during wake-sleep transitions , 2008 .
[25] Josef Kittler,et al. Floating search methods in feature selection , 1994, Pattern Recognit. Lett..
[26] T. Åkerstedt,et al. Reaction of sleepiness indicators to partial sleep deprivation, time of day and time on task in a driving simulator – the DROWSI project , 2009, Journal of sleep research.
[27] Nadine Eberhardt,et al. Bioelectrical Signal Processing In Cardiac And Neurological Applications , 2016 .
[28] Ramesh Rajan,et al. Combining complexity measures of EEG data: multiplying measures reveal previously hidden information , 2015, F1000Research.
[29] E. F. Colecchia,et al. Individual differences in subjective and objective alertness during sleep deprivation are stable and unrelated. , 2003, American journal of physiology. Regulatory, integrative and comparative physiology.
[30] A. Wirz-Justice,et al. Power density in theta/alpha frequencies of the waking EEG progressively increases during sustained wakefulness. , 1995, Sleep.
[31] David Sandberg. Detecting Driver Sleepiness , 2011 .
[32] T. Åkerstedt,et al. Having to stop driving at night because of dangerous sleepiness – awareness, physiology and behaviour , 2013, Journal of sleep research.
[33] Arthur L. Samuel,et al. Some Studies in Machine Learning Using the Game of Checkers , 1967, IBM J. Res. Dev..
[34] Daniel Aeschbach,et al. Dynamics of the human EEG during prolonged wakefulness: evidence for frequency-specific circadian and homeostatic influences , 1997, Neuroscience Letters.
[35] Shuyan Hu,et al. Driver drowsiness detection with eyelid related parameters by Support Vector Machine , 2009, Expert Syst. Appl..
[36] Charles Lincoln Kenji Yamamura. MACHINE LEARNING, INTERNET OF THINGS AND THE FUZZY FRONT END OF PRODUCT DEVELOPMENT , 2017 .
[37] Arcady A. Putilov,et al. The first and second principal components of the EEG spectrum as the correlates of sleepiness , 2012, Somnologie - Schlafforschung und Schlafmedizin.
[38] Srdjan Kesic,et al. Application of Higuchi's fractal dimension from basic to clinical neurophysiology: A review , 2016, Comput. Methods Programs Biomed..
[39] Peter A. Flach,et al. Machine Learning - The Art and Science of Algorithms that Make Sense of Data , 2012 .
[40] J. Richman,et al. Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.
[41] S M Pincus,et al. Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.