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Highly Sensitive Perovskite Photoplethysmography Sensor for Blood Glucose Sensing Using Machine Learning Techniques
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Highly Sensitive Perovskite Photoplethysmography Sensor for Blood Glucose Sensing Using Machine Learning Techniques
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Journal Article
Highly Sensitive Perovskite Photoplethysmography Sensor for Blood Glucose Sensing Using Machine Learning Techniques
2024
Overview
Accurate non‐invasive monitoring of blood glucose (BG) is a challenging issue in the therapy of diabetes. Here near‐infrared (NIR) photoplethysmography (PPG) sensor based on a vapor‐deposited mixed tin‐lead hybrid perovskite photodetector is developed. The device shows a high detectivity of 5.32 × 1012 Jones and a large linear dynamic range (LDR) of 204 dB under NIR light, guaranteeing accurate extraction of eleven features from the PPG signal. By a combination of machine learning, accurate prediction of blood glucose level with mean absolute relative difference (MARD) as small as 2.48% is realized. The self‐powered PPG sensor also works for real‐time outdoor healthcare monitors using sunlight as a light source. The potential for early diabetes diagnoses by the perovskite PPG sensor is demonstrated. A mixed tin‐lead hybrid perovskite photodetector by vapor deposition method is presented, which shows high detectivity and LDR under the NIR region. The superior photoresponse stability guarantees the repeatable and accurate measurement of the PPG signal with blood glucose information and shows the potential of non‐invasive blood glucose detection in near‐infrared applications.
Publisher
John Wiley & Sons, Inc,John Wiley and Sons Inc,Wiley
Subject
