D-Alarm: An Efficient Driver Drowsiness Detection and Alarming System

In the coming years, the demand for IoT devices connecting our homes, cities, and industries will grow significantly. We now inhabit a world where interconnected smart devices can be controlled through single commands, fostering an era of automation, particularly in smart homes. IoT is at the forefront of this transformation, with many of these devices relying on machine learning models to decode data and make accurate predictions. However, as the number of connected devices escalates, network congestion becomes a challenge. To address this, incorporating machine learning intelligence into edge devices is essential. Edge computing, which involves processing data closer to its source, reduces latency and enables faster analysis. Consequently, IoT data can be gathered and processed at the edge, reducing the reliance on cloud-based solutions. Yet, deploying machine learning models on small IoT microcontroller units presents memory and computation constraints. This paper introduces a deep learning model for driver engagement recognition in real-time video streams, alongside optimization techniques for deployment on edge devices like Nvidia Jetson. The goal is to facilitate the execution of machine learning models on compact, low-power integrated circuits, addressing this critical bottleneck.