Machine Learning Trained Drowsiness Detection Using Gyroscope on a Microcontroller

Machine Learning Trained Drowsiness Detection Using Gyroscope on a Microcontroller
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© 2023 by IJETT Journal
Volume-71 Issue-5
Year of Publication : 2023
Author : Bagas Aditya Putra, Valerius Owen, Antoni Wibowo
DOI : 10.14445/22315381/IJETT-V71I5P234

How to Cite?

Bagas Aditya Putra, Valerius Owen, Antoni Wibowo, "Machine Learning Trained Drowsiness Detection Using Gyroscope on a Microcontroller," International Journal of Engineering Trends and Technology, vol. 71, no. 5, pp. 328-335, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I5P234

Abstract
In more recent years, drowsy driving has resulted in 328,000 incidents annually. Drowsiness itself is usually a sign of fatigue, and drivers who experience this may also experience temporary episodes of microsleep that can be fatal while driving. Realizing that some car manufacturers have implemented this, it has only been applied to higher-end cars. From this, a drowsiness detection device needs to be created that is portable and can be used by anyone of any background. A system is designed using a microcontroller and implemented as an Artificial Neural Network with a small size and high accuracy, ported into a header file using Tensorflow Lite. With this, the device is able to detect the drowsiness of the user using the gyroscope from the head movement and uses an LED as well as a buzzer to alert the user or others of drowsiness for real-time predictions.

Keywords
Artificial neural network, Drowsiness, Gyroscope, Machine learning, Microcontroller.

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