Robust and Remote Photoplethysmography Based on Smartphone Imaging of the Human Palm

Arrhythmia is a marked symptom of many cardiovascular diseases (CVDs). Fast and accurate measurement of heart rate (HR) can lead to prompt detection of arrhythmia, which could be potentially life-threatening. However, it remains a challenge to robustly and remotely measure HR due to the typically changing ambient conditions surrounding human subjects. In this study, we propose a method to accurately measure human HR by simply using a smartphone camera. We use three special approaches for HR estimation. First, we fuse the raw data by subtracting the $G$ channel from the $R$ channel to enhance the heartbeat signal from video data. Second, instead of the entire palm, we select a region of interest (ROI) from the palm for HR detection based on signal-to-noise ratio (SNR) maps, which reduces the proportion of the weak signal area. Third, after applying fast Fourier transform (FFT) analyses of the time-series data from the video, we set an interval threshold based on the HR range to accurately determine HR. With a reference HR value measured by electrocardiography (ECG), the detection rate of the proposed method is 95.84% (overall result from 30 test subjects) when used for measurement at an ambient light intensity of 150 lux within a distance of 4.5 m. The method's strong adaptability to changing ambient conditions and ease of implementation makes it applicable to many scenarios, such as homes of the elderly, classrooms, and other public spaces.