Mobile Remote Photoplethysmography: A Real-Time Realization Perspective

Remote photoplethysmography (rPPG) sensors have attracted a significant amount of attention as they enable the remote monitoring of instantaneous heart rates (HRs) and thus do not require any additional devices to be worn on fingers or wrists. In this study, we mounted rPPG sensors on a robot for active and autonomous instantaneous HR (R-AAIH) estimation. Subsequently, we proposed the algorithm providing accurate instantaneous HRs, which can be performed in real time with vision and robot manipulation algorithms. By simplifying the extraction of facial skin images using saturation (S) values in the HSV color space, and selecting pixels based on the most frequent S value on the face image, we achieved reliable HR assessment. The results of the proposed algorithm using the R-AAIH were evaluated by rigorous comparison with the results of existing algorithms on the UBFC-RPPG dataset (n = 42). Our algorithm exhibited an average absolute error (AAE) of 0.71 beats per minute (bpm). The developed algorithm is simple and the processing time is less than 1 s (275 ms for an 8-s window). The algorithm was further validated on our own dataset (BAMI-RPPG dataset [n = 14]) with an AAE of 0.82 bpm.