Drowsiness detection via gesture recognition

OData support
Dr. Györke Péter
Department of Measurement and Information Systems

Drowsiness is part of our lives. However, we do not pay enough attention to it in the fast-paced and information based world. Consequently, this can lead to tragedy. Drowsy drivers cause car accidents, and also we can observe a significant performance decrease in case of sleepy individuals during everyday activities.

Qualitative methods can measure the level of sleepiness, with appropriate monitoring we can predict the onset of the sleep. Due to the evolution of the smartphones, one is able to own a device that can record video and possess sufficient computing capacity to real-time process it.

Currently, there is no widely available product or service that is accurately capable of predicting the level of sleepiness by interacting with the user.

My thesis work is about a conceptual system. The principle is a mobile application that communicates with the user via touch-based or visual gestures. The collected information is evaluated, and by using sleepiness scales the application will be able to predict the level of sleepiness of the user.


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