Algorithms for Monitoring Heart Rate and Respiratory Rate From the Video of a User's Face

• Published in: IEEE journal of translational engineering in health and medicine Vol. 6; pp. 1 - 11
• Main Authors: Sanyal, Shourjya, Nundy, Koushik Kumar
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cameras; Color; Face; Forehead; Heart rate; hue; Medical electronics; Monitoring; Optical sensors; Optical variables measurement; photoplethysmography; Post-processing; Recording; Respiratory rate; Signal processing; smartphone; smartphone camera; Smartphones; Telemedicine; Heart rate; smartphone camera; hue; smartphone; photoplethysmography; respiratory rate
• ISSN: 2168-2372; 2168-2372
• DOI: 10.1109/JTEHM.2018.2818687

Smartphone cameras can measure heart rate (HR) by detecting pulsatile photoplethysmographic (iPPG) signals from post-processing the video of a subject's face. The iPPG signal is often derived from variations in the intensity of the green channel as shown by Poh et. al. and Verkruysse et. al.. In this pilot study, we have introduced a novel iPPG method where by measuring variations in color of reflected light, i.e., Hue, and can therefore measure both HR and respiratory rate (RR) from the video of a subject's face. This paper was performed on 25 healthy individuals (Ages 20-30, 15 males and 10 females, and skin color was Fitzpatrick scale 1-6). For each subject we took two 20 second video of the subject's face with minimal movement, one with flash ON and one with flash OFF. While recording the videos we simultaneously measuring HR using a Biosync B-50DL Finger Heart Rate Monitor, and RR using self-reporting. This paper shows that our proposed approach of measuring iPPG using Hue (range 0-0.1) gives more accurate readings than the Green channel. HR/Hue (range 0-0.1) (r = 0.9201,p-value = 4.1617, and RMSE = 0.8887) is more accurate compared with HR/Green (r = 0.4916,p-value = 11.60172, and RMSE = 0.9068). RR/Hue (range 0-0.1) (r = 0.6575, p-value = 0.2885, and RMSE = 3.8884) is more accurate compared with RR/Green (r = 0.3352, p-value = 0.5608, and RMSE = 5.6885). We hope that this hardware agnostic approach for detection of vital signals will have a huge potential impact in telemedicine, and can be used to tackle challenges, such as continuous non-contact monitoring of neo-natal and elderly patients. An implementation of the algorithm can be found at https://pulser.thinkbiosolution.com