Drowsiness detection in real-time via convolutional neural networks and transfer learning

In plain language

This research investigates real-time drowsiness detection using convolutional neural networks (CNN) and transfer learning. The study developed a user-friendly mobile application that incorporates these advanced techniques. The system was evaluated using diverse datasets, demonstrating its effectiveness in both multi-class and binary classification scenarios. It achieved impressive accuracy rates ranging from 90% to 99.86%. This work contributes to the academic understanding of drowsiness detection and highlights the successful implementation of these methodologies in practical, real-world settings through the developed application.

Key takeaways

Drowsiness detection is crucial for safety in areas like transportation and online learning.
The research employed convolutional neural networks and transfer learning for detecting drowsiness.
A user-friendly mobile application was developed to implement the detection system in real-time.
The system achieved high accuracy, between 90% and 99.86%, for both multi-class and binary classification.
The study successfully demonstrated the practical application of these methodologies in real-world scenarios.

Why it matters

Detecting drowsiness in real-time is vital for preventing accidents and improving safety across various sectors, including driving and online activities. This research offers a practical, high-accuracy solution that could help mitigate risks associated with fatigue, making environments safer for individuals.

Commercialisation angle

This research has clear application pathways for real-time drowsiness detection. The developed mobile application could be used by individuals in transportation, online learning, or multimedia consumption to monitor their alertness. The technology appears to be applied and tested, with a functional application demonstrating its readiness for real-world scenarios.

AI-generated from the published abstract. Always read the original work before citing.

Abstract

Abstract Drowsiness detection is a critical aspect of ensuring safety in various domains, including transportation, online learning, and multimedia consumption. This research paper presents a comprehensive investigation into drowsiness detection methods, with a specific focus on utilizing convolutional neural networks (CNN) and transfer learning. Notably, the proposed study extends beyond theoretical exploration to practical application, as we have developed a user-friendly mobile application incorporating these advanced techniques. Diverse datasets are integrated to systematically evaluate the implemented model, and the results showcase its remarkable effectiveness. For both multi-class and binary classification scenarios, our drowsiness detection system achieves impressive accuracy rates ranging from 90 to 99.86%. This research not only contributes to the academic understanding of drowsiness detection but also highlights the successful implementation of such methodologies in real-world scenarios through the development of our application.

Research topics

Sleep and Work-Related Fatigue
Non-Invasive Vital Sign Monitoring
EEG and Brain-Computer Interfaces

Discussion

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