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

• Published in: IEEE internet of things journal Vol. 11; no. 23; pp. 38767 - 38777
• Main Authors: Ngo, Duc Thang, Tseng, Yen-Ju, Nguyen, Duc Huy, Chao, Paul C.-P.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Artificial neural networks; Band-pass filters; Bandpass filters; Biological system modeling; Biometric; Biometric identification; Biometrics; Blood vessels; Blood volume; Deep learning; deep learning (DL); Direct current; Machine learning; Measuring instruments; Noise measurement; noninvasive; Photodiodes; photoplethysmography (PPG); Power spectral density; Quality assessment; quality check (QC); Real time; Real-time systems; Signal quality; Spectral emittance; Time measurement

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...