Citation

Huang AC, Yuan C, Meng SH, Huang TJ. Big Data 2023; 11(1): 1-17.

Copyright

(Copyright © 2023, Mary Ann Liebert Publishers)

DOI

10.1089/big.2021.0166

PMID

36787408

Abstract

Chronic fatigue symptoms of jobs are risk factors that may cause errors and lead to occupational accidents. For instance, occupational injuries and traffic accidents stem from overlooking long-term fatigue. According to statistics for fatigue driving, it was found that fatigue driving is one of the main causes of traffic accidents. The resulting decrease in the quality of traffic, as well as impaired traffic flow efficiency and functioning, contributes markedly to the societal costs of fatigue. This article proposes a noninvasive physical method for fatigue detection using a machine vision image algorithm. The main technology was implemented using a software framework based on optimized skin color segmentation and edge detection, as well as eye contour extraction. By integrating machine vision and an optimized Hove transform algorithm, our method mainly identifies fatigue based on the detected target's face, head gestures, mouth aspect ratio (MAR), and eye condition, and then triggers an alarm through an intelligent auxiliary device. Our evaluation results of facial image data analysis showed that with an ideal eye threshold of 0.3, PERCLOS-80 standard, MAR, and head gesture-nod frequency, the method can be used to detect fatigue data accurately and systematically, thereby fulfilling the purpose of alerting a group of high-risk drivers and preventing them from engaging in high-risk activities in an involuntary state.

Language: en

Keywords

Risk Factors; Algorithms; Software; *Automobile Driving; Accidents, Traffic/prevention & control; canny edge detector; circle Hough transform; driver behavior correlation analysis; fatigue evaluation; head gestures