Mental Fatigue Identification and Prevention of Accidents in Driving via EEG
DOI:
https://doi.org/10.54228/mjaret0823008Keywords:
Gesture Recognition, Glove, Sign Language, Sensor, Man-Machine Interface, Pattern RecognitionAbstract
Monitoring driver attention has a direct effect on decreasing injury/fatality rates. Hence, detecting episodes of driver distraction is fundamental in reducing car crashes. Driverinattention is a major factor in most traffic accidents. Research and development have actively been carried out for decades, with the goal of precisely determining the driver’s state of mind. In this project, we summarize these approaches. An accident, of which one important characteristic is suddenness, i.e., the occurrence is instantaneously, often cause casualties and variouslosses of society. Distracted driving might lead to many catastrophic consequences. Developing a countermeasure to track driver’s focus ofattention and engagement of operators in dual multi-tasking conditions is thus imperative. Here we have use EEG sensor then it can able to monitor the driver situation. Driver situation means driver slept or not as well as driver concentration while driving. In this system we have to add an alcohol detection system. If driver is drunk then vehicle not starts.
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