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Article

Embedded System Performance Analysis for Implementing a Portable Drowsiness Detection System for Drivers

1
Monta Vista High School, Cupertino, CA 95014, USA
2
Cupertino High School, Cupertino, CA 95014, USA
*
Authors to whom correspondence should be addressed.
Technologies 2023, 11(1), 8; https://doi.org/10.3390/technologies11010008
Submission received: 16 November 2022 / Revised: 25 November 2022 / Accepted: 27 December 2022 / Published: 30 December 2022
(This article belongs to the Special Issue Image and Signal Processing)

Abstract

Drowsiness on the road is a widespread problem with fatal consequences; thus, a multitude of systems and techniques have been proposed. Among existing methods, Ghoddoosian et al. utilized temporal blinking patterns to detect early signs of drowsiness, but their algorithm was tested only on a powerful desktop computer, which is not practical to apply in a moving vehicle setting. In this paper, we propose an efficient platform to run Ghoddoosian’s algorithm, detail the performance tests we ran to determine this platform, and explain our threshold optimization logic. After considering the Jetson Nano and Beelink (Mini PC), we concluded that the Mini PC is most efficient and practical to run our embedded system in a vehicle. To determine this, we ran communication speed tests and evaluated total processing times for inference operations. Based on our experiments, the average total processing time to run the drowsiness detection model was 94.27 ms for the Jetson Nano and 22.73 ms for the Beelink (Mini PC). Considering the portability and power efficiency of each device, along with the processing time results, the Beelink (Mini PC) was determined to be most suitable. Additionally, we propose a threshold optimization algorithm, which determines whether the driver is drowsy, or alert based on the trade-off between the sensitivity and specificity of the drowsiness detection model. Our study will serve as a crucial next step for drowsiness detection research and its application in vehicles. Through our experiments, we have determined a favorable platform that can run drowsiness detection algorithms in real-time and can be used as a foundation to further advance drowsiness detection research. In doing so, we have bridged the gap between an existing embedded system and its actual implementation in vehicles to bring drowsiness technology a step closer to prevalent real-life implementation.
Keywords: drowsiness detection; embedded systems; WebRTC; AioRTC; facial detection; blink detection drowsiness detection; embedded systems; WebRTC; AioRTC; facial detection; blink detection

Share and Cite

MDPI and ACS Style

Kim, M.; Koo, J. Embedded System Performance Analysis for Implementing a Portable Drowsiness Detection System for Drivers. Technologies 2023, 11, 8. https://doi.org/10.3390/technologies11010008

AMA Style

Kim M, Koo J. Embedded System Performance Analysis for Implementing a Portable Drowsiness Detection System for Drivers. Technologies. 2023; 11(1):8. https://doi.org/10.3390/technologies11010008

Chicago/Turabian Style

Kim, Minjeong, and Jimin Koo. 2023. "Embedded System Performance Analysis for Implementing a Portable Drowsiness Detection System for Drivers" Technologies 11, no. 1: 8. https://doi.org/10.3390/technologies11010008

APA Style

Kim, M., & Koo, J. (2023). Embedded System Performance Analysis for Implementing a Portable Drowsiness Detection System for Drivers. Technologies, 11(1), 8. https://doi.org/10.3390/technologies11010008

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