Continuous-Spectrum Infrared Illuminator for Camera-PPG in Darkness

Modified Camera Setups for Day-and-Night Pulse-rate Monitoring

Camera systems have been studied as a means for ubiquitous remote photoplethysmography. It was first considered for daytime applications using ambient light. However, main applications for continuous monitoring are in dark or low-light conditions (e.g. sleep monitoring) and, more recently, suitable light sources and simple camera adaptations have been considered for infrared-based solutions. This paper explores suitable camera configurations for pulse-rate monitoring during both day and night (24/7). Various configurations differing in the recorded spectral range are defined, i.e. straight-forward adaptations of a standard RGB camera by choosing proper optical filters. These systems have been studied in a benchmark involving day and night monitoring with various degrees of motion disturbances. The results indicate that, for the 24/7 monitoring, it is best to deploy the full spectral band of an RGB camera, and this can be done without compromising the monitoring performance at night.

Adaptive Gaussian Mixture Model Driven Level Set Segmentation for Remote Pulse Rate Detection

This paper presents an approach for pulse rate extraction from videos. The core of the presented approach is a novel method to segment and track a suitable region of interest (ROI). The proposed method combines level sets with subject-individual Gaussian Mixture Models to yield a time varying ROI. The ROI builds up from multiple homogeneous skin areas under constraints regarding the area and contour length of the ROI. Together with state of the art signal processing methods our approach yields an Mean Average Error (MAE) of 2.3 bpm, 1.4 bpm and 2.7 bpm on own data, the PURE database and the UBFC-rPPG database, respectively. Therewith, our method performs equal or better compared to widely used approaches (e.g. the KLT tracker instead of the proposed image processing yields an MAE of 2.6 bpm, 2.6 bpm and 4.4 bpm). Such results and the 2nd place with a MAE of 7.92 bpm in the 1st Challenge on Remote Physiological Signal Sensing prove the applicability of the proposed method. The taken approach, however, bears further potential for optimization in the context of photoplethysmography imaging and should be transferable to other segmentation tasks as well.