Citation

Inkeaw P, Srikummoon P, Chaijaruwanich J, Traisathit P, Awiphan S, Inchai J, Worasuthaneewan R, Theerakittikul T. Nat. Sci. Sleep 2022; 14: 1641-1649.

Copyright

(Copyright © 2022, Dove Press)

DOI

10.2147/NSS.S376755

PMID

36132745

PMCID

PMC9482962

Abstract

PURPOSE: Driving while drowsy is a major cause of traffic accidents globally. Recent technologies for detection and alarm within automobiles for this condition are limited by their reliability, practicality, cost, and lack of clinical validation. In this study, we developed an early drowsiness detection algorithm and device based on the "gold standard brain biophysiological signal" and facial expression digital data.

METHODS: The data were obtained from 10 participants. Artificial neural networks (ANN) were adopted as the model. Composite features of facial descriptors (ie, eye aspect ratio (EAR), mouth aspect ratio (MAR), face length (FL), and face width balance (FWB)) extracted from two-second video frames were investigated.

RESULTS: The ANN combined with the EAR and MAR features had the most sensitivity (70.12%) while the ANN combined with the EAR, MAR, and FL features had the most accuracy and specificity (60.76% and 58.71%, respectively). In addition, by applying the discrete Fourier transform (DFT) to the composite features, the ANN combined with the EAR and MAR features again had the highest sensitivity (72.25%), while the ANN combined with the EAR, MAR, and FL features had the highest accuracy and specificity (60.40% and 54.10%, respectively).

CONCLUSION: The ANN with DFT combined with the EAR, MAR, and FL offered the best performance. Our direct driver sleepiness detection system developed from the integration of biophysiological information and internal validation provides a valuable algorithm, specifically toward alertness level.

Language: en

Keywords

driver sleepiness detection; drowsy driving