Citation

Hirota T, Fujita R, Harada A, Kawamura D, Yamada K. 27th International Technical Conference on the Enhanced Safety of Vehicles (ESV); April 3-6, 2023; Abstract #: 23-0047, pp. 9p. Washington, DC USA: US National Highway Traffic Safety Administration, 2023 open access.

Copyright

(Copyright © 2023 open access, US National Highway Traffic Safety Administration)

Abstract

27th International Technical Conference on the Enhanced Safety of Vehicles (ESV): Enhanced and Equitable Vehicle Safety for All: Toward the Next 50 Years

https://www-esv.nhtsa.dot.gov/Proceedings/27/27ESV-000047.pdf

In order to prevent traffic accidents due to abrupt changes in the driver's health condition, the authors have proposed a non-contact type electrocardiographic sensor that monitors the electrocardiogram (ECG) of a driver holding a steering wheel while seated. However, the heart rate detection accuracy degrades while driving due to the lower signal-to-noise ratio (SNR) of the ECG caused by the noise from vehicle vibration and static electricity, among others. In this study, the authors propose a method of detecting R-peaks of the ECG from the low SNR ECG signal with high accuracy using a multi-channel one-dimensional convolutional neural network with accelerometer signals as an input. As the results, they achieved an F-score of 78.5% and a root-mean-square error (RMSE) of 1.99 ms. The R-peak detection performance was significantly improved when the input data length of around 1100 ms was chosen.

Language: en