Highly Sensitive Perovskite Photoplethysmography Sensor for Blood Glucose Sensing Using Machine Learning Techniques
Year: 2024
Type: article
Abstract: 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.
Source: Advanced Science
Authors: Yongjian Zheng, Zhenye Zhan, Qiulan Chen, Jianxin Chen, Jianwen Luo, Juntao Cai, Yang Zhou, Ke Chen, Weiguang Xie
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $6,120
Awards: G2787915274, G7132518568
OpenAlex ID: W4402713265