TY - CHAP U1 - Konferenzveröffentlichung A1 - Gromer, Markus A1 - Salb, David A1 - Walzer, Thomas A1 - Martínez Madrid, Natividad A1 - Seepold, Ralf T1 - ECG sensor for detection of driver’s drowsiness T2 - 23rd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2019), 4 - 6 September 2019, Budapest, Hungary; (Procedia Computer Science) N2 - 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 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 the drowsiness detection. The work includes the hardware and the software design. The hardware has been 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, that combined 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 compact resulting 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. KW - ECG KW - Drowsiness KW - Automotive KW - Sleep KW - Biomedical Signal Capturing Y1 - 2019 UN - https://nbn-resolving.org/urn:nbn:de:bsz:kon4-opus4-21587 SN - 1877-0509 SS - 1877-0509 U6 - https://doi.org/10.1016/j.procs.2019.09.366 DO - https://doi.org/10.1016/j.procs.2019.09.366 VL - 159 SP - 1938 EP - 1946 ER -