It is known that 33% of traffic accidents worldwide are caused by drunk driving or drowsiness  , so a drowsiness level detection system that integrates image processing was developed with the use of Raspberry Pi3 with the OpenCV library; and sensors such as MQ-3 that measures the percentage of alcohol and the S9 sensor that measures the heart rate. In addition, it has an alert system and as an interface for the visualization of the data measured by the sensors a touch screen. With the image processing technique, facial expressions are analyzed, while physiological behaviors such as heart rate and alcohol percentage are measured with the sensors. In image test training you get an accuracy of x in a response time of x seconds. On the other hand, the evaluation of the operation of the sensors in 90% effective. So the method developed is effective and feasible.
|Idioma original||Inglés estadounidense|
|Título de la publicación alojada||2019 IEEE 39th Central America and Panama Convention, CONCAPAN 2019|
|Editorial||Institute of Electrical and Electronics Engineers Inc.|
|ISBN (versión digital)||9781728108834|
|Estado||Indizado - nov. 2019|
|Publicado de forma externa||Sí|
|Evento||39th IEEE Central America and Panama Convention, CONCAPAN 2019 - Guatemala City, Guatemala|
Duración: 20 nov. 2019 → 22 nov. 2019
Serie de la publicación
|Nombre||2019 IEEE 39th Central America and Panama Convention, CONCAPAN 2019|
|Conferencia||39th IEEE Central America and Panama Convention, CONCAPAN 2019|
|Período||20/11/19 → 22/11/19|
Nota bibliográficaPublisher Copyright:
© 2019 IEEE.