Abstract
Camera-based remote photoplethysmography (rPPG) is a low-cost and casual non-contact heart rate measurement method suitable for telemedicine. Several factors affect the accuracy of measuring the heart rate and heart rate variability (HRV) using rPPG despite HRV being an important indicator for healthcare monitoring. This study aimed to investigate the appropriate setup for precise HRV measurements using rPPG while considering the effects of possible factors including illumination, direction of the light, frame rate of the camera, and body motion. In the lighting conditions experiment, the smallest mean absolute R–R interval (RRI) error was obtained when light greater than 500 lux was cast from the front (among the following conditions—illuminance: 100, 300, 500, and 700 lux; directions: front, top, and front and top). In addition, the RRI and HRV were measured with sufficient accuracy at frame rates above 30 fps. The accuracy of the HRV measurement was greatly reduced when the body motion was not constrained; thus, it is necessary to limit the body motion, especially the head motion, in an actual telemedicine situation. The results of this study can act as guidelines for setting up the shooting environment and camera settings for rPPG use in telemedicine.
Highlights
Various telemedicine services have garnered interest with the development of information and communication technology (ICT) [1,2]
To monitor the heart rate variability (HRV), remote photoplethysmogram techniques [8,9,10], remote photoplethysmography (rPPG) for short, are expected to be a casual non-contact method that can be implemented in telemedicine services because they do not require dedicated wearable devices or electrode pads required for photoplethysmogram (PPG) [11] and electrocardiogram (ECG) measurements
This study examined the effects of lighting environment, camera frame rate, and body motion on the accuracy of R–R interval (RRI) and HRV measurements using rPPG in the context of telemedicine
Summary
Various telemedicine services have garnered interest with the development of information and communication technology (ICT) [1,2]. Several types of telemedicine services using biological signals, such as blood pressure [3], blood glucose [4], ECG, SpO2, and temperature [5] have been proposed as healthcare indicators to prevent critical illness and frailty. Yamazaki et al [3] proposed a robotic system to monitor the blood pressure and heart rate of a patient at home using a sphygmomanometer. [4] proposed a cloud service that manages blood glucose meters and insulin doses for diabetic patients and is used for in-hospital consultations and remote guidance. The heart rate and its variability obtained by rPPG are used as indicators for patient monitoring [12], sleep monitoring [13], and neonate monitoring [14]
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