Paper
3 April 2015 Nanosensor system for monitoring brain activity and drowsiness
Author Affiliations +
Abstract
Detection of drowsiness in drivers to avoid on-road collisions and accidents is one of the most important applications that can be implemented to avert loss of life and property caused by accidents. A statistical report indicates that drowsy driving is equally harmful as driving under influence of alcohol. This report also indicates that drowsy driving is the third most influencing factor for accidents and 30% of the commercial vehicle accidents are caused because of drowsy driving. With a motivation to avoid accidents caused by drowsy driving, this paper proposes a technique of correlating EEG and EOG signals to detect drowsiness. Feature extracts of EEG and blink variability from EOG is correlated to detect the sleepiness/drowsiness of a driver. Moreover, to implement a more pragmatic approach towards continuous monitoring, a wireless real time monitoring approach has been incorporated using textile based nanosensors. Thereby, acquired bio potential signals are transmitted through GSM communication module to the receiver continuously. In addition to this, all the incorporated electronics are equipped in a flexible headband which can be worn by the driver. With this flexible headband approach, any intrusiveness that may be experienced by other cumbersome hardware is effectively mitigated. With the continuous transmission of data from the head band, the signals are processed on the receiver side to determine the condition of the driver. Early warning of driver’s drowsiness will be displayed in the dashboard of the vehicle as well as alertness voice and sound alarm will be sent via the vehicle radio.
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Mouli Ramasamy, Vijay K. Varadan, and Robert Harbaugh "Nanosensor system for monitoring brain activity and drowsiness", Proc. SPIE 9434, Nanosensors, Biosensors, and Info-Tech Sensors and Systems 2015, 94340A (3 April 2015); https://doi.org/10.1117/12.2177286
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KEYWORDS
Signal processing

Electroencephalography

Electrodes

Signal detection

Receivers

Head

Linear filtering

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