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ECG sensor for detection of driver’s drowsiness
Author(s) -
Markus Gromer,
David Salb,
Thomas Walzer,
Natividad Martínez Madrid,
Ralf Seepold
Publication year - 2019
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2019.09.366
Subject(s) - computer science , qrs complex , artificial intelligence , computer hardware , real time computing , frame rate , computer vision , software , arduino , pattern recognition (psychology) , embedded system , medicine , cardiology , programming language
Fatigue and drowsiness are responsible for a significant percentage of road traffic accidents. There are several approaches to monitor the driver’s drowsiness, ranging from the driver’s steering behavior to the analysis of the driver, e.g., eye tracking, blinking, yawning, or electrocardiogram (ECG). This paper describes the development of a low-cost ECG sensor to derive heart rate variability (HRV) data for drowsiness detection. The work includes hardware and software design. The hardware was implemented on a printed circuit board (PCB) designed so that the board can be used as an extension shield for an Arduino. The PCB contains a double, inverted ECG channel including low-pass filtering and provides two analog outputs to the Arduino, which combines them and performs the analog-to-digital conversion. The digital ECG signal is transferred to an NVidia embedded PC where the processing takes place, including QRS-complex, heart rate, and HRV detection as well as visualization features. The resulting compact sensor provides good results in the extraction of the main ECG parameters. The sensor is being used in a larger frame, where facial-recognition-based drowsiness detection is combined with ECG-based detection to improve the recognition rate under unfavorable light or occlusion conditions.

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