An Under-Screen Fingerprint Sensor for Measurement of PPG and Blood Pressure Estimation

A fingerprint sensor array of pixels with photo-diodes for sensing and thin film transistors (TFTs) as switches was developed for the first time together with new algorithms to estimate blood pressure (BP) based on quality photoplethysmogram (PPG) sensed from this array device. Towards obtaining PPG in high quality, i.e., low noise, the pixels in the sensor array for under-screen fingerprint sensing in mobile devices are designed capable of being operated in a different mode, connected in parallel to output larger current signals than the original mode of fingerprint sensing, for better SNRs (signal-to-noise ratio) of sensed PPGs. The sensed PPG is next pre-processed with being filtered to remove noise, normalized and re-sampled, etc., followed by a quality check engineered to assess further the sensed PPG in real time by a built deep learning (DL) model, a 1-dimensional convolutional neural network (1D-CNN) with Long Short-Term Memory (LSTM). The model in intended to disqualify the PPGs contaminated by ambient light interference and motion artifacts. Data was collected from 88 subjects, including 52 men and 36 women, by measuring PPG signals from a fingerprint sensor, with systolic blood pressure (SBP) ranging from 90 to 150 mmHg while diastolic blood pressure (DBP) from 50 to 85 mmHg. An OMRON HEM-7127 device was used to obtain reference BPs. In results, an accuracy of 97.13% for distinguishing between qualified and unqualified PPGs has been achieved by the afore-mentioned 1D-CNN trained by ground-truths, pre-collected PPGs judged and labeled into 2 groups, qualified and unqualified PPGs. Next, only the qualified PPGs are fed to another DL model built for estimating BP, in a structure of 1D-CNN with 3 consecutive sections of convolution, batch normalization and max pooling. This BP model is next optimized to a structure and parameters to achieve the best combined estimation of SBP and DBP. The results show accuracy with 1.31 (mean absolute error, MAE) ± 1.34 (standard deviation, SD) for SBP and 4.11 (MAE) ± 3.32 (SD) for DBP, favorable as compared to all the prior arts using an image sensor to estimate BPs.