Bp Meter

This application predicts two important physiological vital signs that are blood pressure (BP) and heart rate (HR) from a 20 seconds fingertip non-recorded video stream. The main idea of this application is to use the fingertip video stream to estimate the remote Photoplethysmography (rPPG) signal, which interns is used for monitoring BP and HR. Here we are using our per-beat rPPG-to-BP mapping scheme based on transfer learning. An interesting representation of a 1-D PPG signal as a 2-D image is used for enabling powerful off-theshelf image-based models through transfer learning. It resolves limitations about training data size due to strict data cleaning. The estimated rPPG is used for HR estimation. Then, the rPPG is segmented into beats. Furthermore, double cleaning is applied for training contact PPG data and testing rPPG beats as well. The quality of the segmented beats is tested by checking some of the related quality metrics. Hence, the prediction reliability is enhanced by excluding deformed beats. Varying rPPG quality is relaxed by selecting beats during intervals of the highest signal strength. The high-quality beats is used for BP estimation. Based on the experimental results, the proposed system outperforms the state-of-the-art systems in the sense of mean absolute error (MAE) and standard deviation (STD). STD for the test data is decreased to 5.4782 and 3.8539 for SBP and DBP, respectively. Also, MAE decreased to 2.3453 and 1.6854 for SBP and DBP, respectively. Moreover, the results for BP estimation from real video reveal that the STD reaches 8.027882 and 6.013052 for SBP and DBP, respectively. Also, MAE for the estimated BP from real videos reaches 7.052803 and 5.616028 for SBP and DBP, respectively. For more information, please refer to our paper: https://link.springer.com/article/10.1007/s10489-024-05354-9