Dynamic ROI based on K-means for remote photoplethysmography

Abstract

Remote imaging photoplethysmography (RIPPG) can achieve contactless human vital signs monitoring. Though the remote operation mode brings a great convenience for RIPPG applications, the RIPPG signal quality is limited by the remote nature. Improving the RIPPG signal quality becomes an essential task in the clinical application of RIPPG. Since the region of interest (ROI) of the RIPPG transforms from a point to an area, there is a new approach to improving the RIPPG signal quality through refining the ROI. In this paper, we propose a dynamic ROI for RIPPG, which can automatically select the skin regions corresponding to good quality RIPPG signals. First, a fixed ROI is divided into non-overlapped blocks. Then two features are proposed to perform no-reference quality assessment for RIPPG signals from different blocks. After that, K-means clustering operates in a two dimensional feature space. A dynamic ROI can be selected for a video segment based on the clustering result, updated every two seconds. Nineteen healthy subjects were enrolled to test the proposed ROI selection method on both the facial region and the palmar region. Experimental results of heart rate measurement show that the proposed dynamic ROI method for RIPPG can effectively improve the RIPPG signal quality, compared with the state-of-the-art ROI methods for RIPPG.