The electrooculography (EOG) signal is considered most suitable for drowsiness detection. Besides its simplicity and low cost, EOG signals are not affected by environmental factors such as light intensity and driver movement. However, existing EOG-based drowsiness detection techniques employ arbitrarily chosen features for classifier training, leading to results that are less robust against changes in the measurement method, noise level, and individual subject variability. In this study, we propose a system dynamics-based approach to drowsiness detection. The EOG signal is treated as a neurophysiological response of the oculomotor system. Each blink action is considered as a result of a series of neuron firing impulses entering the system. Blink signatures are thus extracted to identify the system transfer function, from which system poles are computed to characterize the drowsiness state of the subject. It was found that the location of system poles on the pole–zero map for blink signatures from an alert state was distinctly different from those from a drowsy state. A simple criterion was subsequently developed for drowsiness detection by counting the ratio of real and complex poles of the system over any given period of time. The proposed methodology is a systematic approach and does not require extensive classifier training. It is robust against variations in the subject condition, sensor placement, noise level, and blink rate.
Skip Nav Destination
Article navigation
August 2017
Research-Article
Drowsiness Detection With Electrooculography Signal Using a System Dynamics Approach
Dongmei Chen,
Dongmei Chen
Department of Mechanical Engineering,
University of Texas at Austin,
Austin, TX 78712
University of Texas at Austin,
Austin, TX 78712
Search for other works by this author on:
Zheren Ma,
Zheren Ma
Department of Mechanical Engineering,
University of Texas at Austin,
Austin, TX 78712
University of Texas at Austin,
Austin, TX 78712
Search for other works by this author on:
Brandon C. Li,
Brandon C. Li
The Wharton School of Business,
University of Pennsylvania,
Philadelphia, PA 19104
University of Pennsylvania,
Philadelphia, PA 19104
Search for other works by this author on:
Zeyu Yan,
Zeyu Yan
Department of Mechanical Engineering,
University of Texas at Austin,
Austin, TX 78712
University of Texas at Austin,
Austin, TX 78712
Search for other works by this author on:
Wei Li
Wei Li
Department of Mechanical Engineering,
University of Texas at Austin,
Austin, TX 78712
University of Texas at Austin,
Austin, TX 78712
Search for other works by this author on:
Dongmei Chen
Department of Mechanical Engineering,
University of Texas at Austin,
Austin, TX 78712
University of Texas at Austin,
Austin, TX 78712
Zheren Ma
Department of Mechanical Engineering,
University of Texas at Austin,
Austin, TX 78712
University of Texas at Austin,
Austin, TX 78712
Brandon C. Li
The Wharton School of Business,
University of Pennsylvania,
Philadelphia, PA 19104
University of Pennsylvania,
Philadelphia, PA 19104
Zeyu Yan
Department of Mechanical Engineering,
University of Texas at Austin,
Austin, TX 78712
University of Texas at Austin,
Austin, TX 78712
Wei Li
Department of Mechanical Engineering,
University of Texas at Austin,
Austin, TX 78712
University of Texas at Austin,
Austin, TX 78712
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received July 25, 2016; final manuscript received December 19, 2016; published online May 15, 2017. Assoc. Editor: Evangelos Papadopoulos.
J. Dyn. Sys., Meas., Control. Aug 2017, 139(8): 081003 (7 pages)
Published Online: May 15, 2017
Article history
Received:
July 25, 2016
Revised:
December 19, 2016
Citation
Chen, D., Ma, Z., Li, B. C., Yan, Z., and Li, W. (May 15, 2017). "Drowsiness Detection With Electrooculography Signal Using a System Dynamics Approach." ASME. J. Dyn. Sys., Meas., Control. August 2017; 139(8): 081003. https://doi.org/10.1115/1.4035611
Download citation file:
Get Email Alerts
Cited By
Dual Neural Network Control of a Hybrid FES Cycling System
J. Dyn. Sys., Meas., Control
Impact of Added Passive Compliance On the Performance of Tip-Actuated Flexible Manipulators
J. Dyn. Sys., Meas., Control
A Load Control Strategy For Stable Operation Of Free-Piston Electromechanical Hybrid Power System
J. Dyn. Sys., Meas., Control
Related Articles
Root Locus Analysis of the Adaptation Process in Active Noise Control for Repetitive Impulses
J. Dyn. Sys., Meas., Control (January,2016)
Adaptive Feedforward Control Applied in Switched Reluctance Machines Drive Speed Control in Fault Situations
J. Dyn. Sys., Meas., Control (May,2018)
Real-Time Bradycardia Prediction in Preterm Infants Using a Dynamic System Identification Approach
ASME J of Medical Diagnostics (February,2020)
Estimation of Dynamical Thermoacoustic Modes Using an Output Only Observer Kalman Filter-Based Identification Algorithm
J. Eng. Gas Turbines Power (May,2023)
Related Proceedings Papers
Related Chapters
Generating Synthetic Electrocardiogram Signals Withcontrolled Temporal and Spectral Characteristics
Intelligent Engineering Systems through Artificial Neural Networks Volume 18
Biosensors
Impedimetric Biosensors for Medical Applications: Current Progress and Challenges
Study on the Method of Multirate Data Processing System
International Conference on Computer Technology and Development, 3rd (ICCTD 2011)