Heart Rate Estimation in Driver Monitoring System Using Quality-Guided Spectrum Peak Screening
正之 冨留宮, Xuezhi Yang, Rencheng Song, Xuesong Han, Chong Ren, Hailin Shi, Jianwei. Niu, Wei Li
Abstract
Remote photoplethysmography (rPPG) enables heart rate (HR) measurement under stable illumination and low noise conditions. However, challenges arise due to rapid lighting changes, significant head movements, and vehicle vibrations during driving, impacting the recovery of clear rPPG signals and rendering denoising techniques alone inadequate in entirely eliminating noise interference. To address these challenges, we introduce an innovative approach, “Quality-guided Spectral Peak Screening” (QSPS), for monitoring driver’s HR during driving. First, we developed a signal evaluation framework for assessing signal quality across multiple facial regions following wavelet filtering. Subsequently, by leveraging quality information, a spectral peak screening algorithm is applied to signals from multiple regions, mitigating the impact of residual noise on rPPG. The precise HR is determined by integrating quality scores and short-term stability from various facial regions. Our method is employed in a commercially available driver monitoring system equipped with a monochrome camera. Test results show QSPS’s robustness compared with established NIR-based HR detection methods. In driving scenarios, QSPS achieves a Mean Absolute Error of 4.32 bpm, a root mean square error of 6.15 bpm, and a percentage of time with HR estimation errors smaller than 6 bpm of 68.8%. Furthermore, QSPS exhibits excellent performance during extended 10-min HR monitoring, with the Pearson correlation coefficient of 0.943 in the night test and 0.906 in the day test.