Sistema de reconocimiento de indicadores de somnolencia mediante inteligencia artificial

Abstract

The lack of sleep not only affects safety but also increases the risk of other health problems. Sleepiness, mainly caused by sleep deprivation, negatively impacts daily human functions, including reaction time, performance, and attention, leading to a decrease in alertness and concentration. In this context, a study was conducted with the aim of implementing an artificial intelligence-based system to recognize signs of sleepiness and issue alerts to individuals in that state, in order to restore their attention and allow them to continue with their activities. The system consists of four stages: acquisition, processing, training, and visualization. In the acquisition stage, the Pi Noir V2 camera was used to capture real-time images or videos. The acquired data was sent to the NVIDIA Jetson Nano for processing. Neural networks were used to train a model capable of accurately recognizing indicators of sleepiness. For practical use and deployment, the system was implemented in a cloud hosting environment. The system's algorithm was developed in Python due to the variety of available libraries, and the OpenCV library was used for image processing due to its wide range of commands. Test results showed that the system processes and sends information at an average time of 2.38 milliseconds for real-time video.