Notice of retraction
Vol. 32, No. 8(2), S&M2292

ISSN (print) 0914-4935
ISSN (online) 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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Smart Driver Drowsiness Detection Model Based on Analytic Hierarchy Process

Ting-Cheng Chang, Min-Hao Wu, Phan-Zhu Kim, and Ming-Hui Yu

(Received July 20, 2020; Accepted November 16, 2020)

Keywords: drowsiness detection, electrocardiogram, ECG, heart rate, new features

This paper proposes a smart driver drowsiness detection (SDDD) model for vehicles. The SDDD monitors a driver’s heart rate variability (HRV) through electrocardiography (ECG) in real time to detect driver drowsiness. The SDDD processes the data of HRV and ECG to obtain a set of parameters with time-domain analysis, frequency-domain analysis, detrended fluctuation analysis, approximate entropy, and sample entropy. In the process, a machine learning algorithm analyzes the parameters to detect driver drowsiness. The SDDD optimizes critical features with the analytic hierarchy process (AHP), which uses a feature extraction method through an iterative procedure. It is found that the SDDD in this study detects the level of driver drowsiness with higher sensitivity than previous models.

Corresponding author: Min-Hao Wu

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