Precision Biometrics Based on the PPG Measured From an IoT Device With OPDs, Real-Time Quality Check Through PSD, DC-Drift and Deep-Learning

A high-accuracy biometric identification system based on photoplethysmography (PPG) is proposed in this study. Equipped with continuous quality assessment on PPG in real-time by calculated power spectral density (PSD) and large-area organic photodetectors (OPDs) in the PPG sensor offering low-noise PPG, the deep learning model built herein is able to acquire delicate PPG features varying clearly from subject to subject, and then achieves high accuracy for biometric applications. It is known that PPG is a technology capable of measuring blood volume changes by emitting optical power into skin, reaching blood vessels and collects the reflected optical power back and out of skin, suitable for ensuring live-body bio-metrics while many other bio-metrics are unable to. The raw PPG measured by the PPG device is first pre-processed by a bandpass filter, and then those with low power spectral density (PSD) of PPG versus noise or large DC drifts are screened out in real time to ensure the signal quality of PPG prior to bio-metrics. This pre-processing step is crucial to disregard all the unqualified PPG that may lead to wrongful result of biometrics later. The bio-metrics is next conducted by a built deep-learning (DL) model of a convolutional neural network (CNN) and long short-term memory (LSTM) layers. The DL model is trained by the PPG data collected from 42 subjects. Experimental results show an accuracy of 99.64% for binary while 98.8% for multi-class classification, outperforming other related works using PPG.