Real-Time Detection of Drowsiness Among Vehicle Drivers: A Machine Learning Algorithm for Embedded Systems

TL;DR

This paper presents a resource-efficient machine learning algorithm for real-time drowsiness detection in drivers using electrooculography, suitable for deployment on portable wearable systems with microcontrollers.

Contribution

A novel, lightweight machine learning algorithm designed for reliable drowsiness detection on resource-constrained embedded systems.

Findings

Achieved high accuracy and precision in detecting prolonged blinks

Demonstrated feasibility of deploying the algorithm on low-cost microcontrollers

Validated the system's effectiveness through multiple testing rounds

Abstract

Numerous studies have established the necessity for developing safety equipment to detect drowsiness among vehicle drivers. However, for reliable implementations, such systems must employ dependable sources of stimuli; through Electrooculography (EOG), the tendencies of drowsiness can be directly sensed by measuring blinks of prolonged durations. While conventional machine learning (ML) algorithms can be utilized for the detection and classification of these prolonged blinks (PB), executing them on microcontroller units (MCU) may prove to be a laborious task. Hence, by keeping resource constraints and practicality in mind, an ML algorithm is proposed in this study to identify PBs executed by an individual with desirable accuracy and precision while being efficient enough to be deployed on portable wearables using economic MCUs. Furthermore, the suggested algorithm is subjected to multiple rounds of testing in this study thereby, establishing its possibility as a feasible drowsiness detection measure for wearable systems.