Sleepiness has been considered as one of the major contributors to driver error that causes many automobile accidents. Among various technologies developed to address this issue, the electrooculography (EOG) signal is considered most suitable for sleepiness detection. It is simple, and resilient to environmental factors such as light intensity and driver movement. Most importantly, the physiological signal changes in an early stage and can be used to detect the on-set of human sleepiness. In this paper, we introduce the development of a wearable sleepiness detection system based on analyzing EOG signal dynamics. The system includes wearable sensors, amplifying and transmitting circuits, and a smart phone that could alarm the driver if sleepiness is detected. In this system, the EOG signal is considered as a neurophysiological response of the oculomotor system. Blink signatures are extracted from the EOG signal. It was found that the poles of a linearized blinking motion associated with an alert state are different from those associated with a sleepy state. Based on this understanding, an algorithm to detect the driver’s sleepiness was developed. A proof of concept device design has been completed. This system will help a driver to correct the behavior, and ultimately saves lives.
- Dynamic Systems and Control Division
Wearable Sleepiness Detection Based on Characterization of Physiological Dynamics
Ma, Z, Li, BC, Yan, Z, Chen, D, & Li, W. "Wearable Sleepiness Detection Based on Characterization of Physiological Dynamics." Proceedings of the ASME 2016 Dynamic Systems and Control Conference. Volume 1: Advances in Control Design Methods, Nonlinear and Optimal Control, Robotics, and Wind Energy Systems; Aerospace Applications; Assistive and Rehabilitation Robotics; Assistive Robotics; Battery and Oil and Gas Systems; Bioengineering Applications; Biomedical and Neural Systems Modeling, Diagnostics and Healthcare; Control and Monitoring of Vibratory Systems; Diagnostics and Detection; Energy Harvesting; Estimation and Identification; Fuel Cells/Energy Storage; Intelligent Transportation. Minneapolis, Minnesota, USA. October 12–14, 2016. V001T10A004. ASME. https://doi.org/10.1115/DSCC2016-9849
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