A Heart Rate Monitoring Framework for Real-World Drivers Using Remote Photoplethysmography

Po Wei Huang*, Bing Jhang Wu, Bing Fei Wu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


Remote photoplethysmography (rPPG) is an unobtrusive solution to heart rate monitoring in drivers. However, disturbances that occur during driving such as driver behavior, motion artifacts, and illuminance variation complicate the monitoring of heart rate. Faced with disturbance, one commonly used assumption is heart rate periodicity (or spectrum sparsity). Several methods improve stability at the expense of tracking sensitivity for heart rate variation. Based on statistical signal processing (SSP) and Monte Carlo simulations, the outlier probability is derived and ADaptive spectral filter banks (AD) is proposed as a new algorithm which provides an explicable tuning option for spectral filter banks to strike a balance between robustness and sensitivity in remote monitoring for driving scenarios. Moreover, we construct a driving database containing over 23 hours of data to verify the proposed algorithm. The influence on rPPG from driver habits (both amateurs and professionals), vehicle types (compact cars and buses), and routes are also evaluated. In comparison to state-of-the-art rPPG for driving scenarios, the mean absolute error in the Passengers, Compact Cars, and Buses scenarios is 3.43, 7.85, and 5.02 beats per minute, respectively. Moreover, AD also won the top third place in the first challenge on remote physiological signal sensing (RePSS) with relative low computational complexity.

Original languageEnglish
Article number9205299
Pages (from-to)1397-1408
Number of pages12
JournalIEEE Journal of Biomedical and Health Informatics
Issue number5
StatePublished - May 2021


  • Advanced driver assistance systems
  • heart rate monitoring
  • remote photoplethysmography


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