Driver assistance systems are a major focus of the automotive industry. Although technological functions that help drivers are improving, the monitoring of driver state functions receives less attention. In this respect, the human heart rate (HR) is one of the most important bio-signals, and it can be detected remotely using consumer-grade cameras. Based on this, a video-based driver state monitoring system using HR signals is proposed in this paper. In a practical automotive environment, monitoring the HR is very challenging due to changes in illumination, vibrations, and human motion. In order to overcome these problems, source separation strategies were employed using joint blind source separation, and feature combination was adopted to maximize HR variation. Noise-assisted data analysis was then adopted using ensemble empirical mode decomposition to extract the pure HR. Finally, power spectral density analysis was conducted in the frequency domain, and a post-processing smoothing filter was applied. The performance of the proposed approach was tested based on commonly employed metrics using the MAHNOB-HCI public dataset and compared with recently proposed competing methods. The experimental results proved that our method is robust for a variety of driving conditions based on testing using a driving dataset and static indoor environments.
Comparison of Blind Source Separation Algorithms for Optical Heart Rate Monitoring