Highly Sensitive Perovskite Photoplethysmography Sensor for Blood Glucose Sensing Using Machine Learning Techniques
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 × 10<sup>12</sup> 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.
Authors
- Zhenye Zhan
- Qiulan Chen (ORCID: https://orcid.org/0000-0001-6711-2304)
- Yang Zhou (ORCID: https://orcid.org/0000-0001-6972-4822)
- Juntao Cai (ORCID: https://orcid.org/0000-0003-4132-3167)
- Weiguang Xie (ORCID: https://orcid.org/0000-0002-3706-6359)
- Yongjian Zheng
- Jianwen Luo (ORCID: https://orcid.org/0000-0002-1594-3867)
- Jianxin Chen (ORCID: https://orcid.org/0000-0001-5631-3936)
- Ke Chen (ORCID: https://orcid.org/0000-0002-6093-6623)
Institutions
- Jinan University (CN)
- Guangzhou Research Institute of Non-ferrous Metals (CN)
- Guangdong Food and Drug Vocational College (CN)
- First Affiliated Hospital of Jinan University (CN)
Publication Details
- Journal
- Advanced Science
- Published
- 2024-09-20
- DOI
- https://doi.org/10.1002/advs.202405681
- Citations
- 21
- Primary Topic
- Non-Invasive Vital Sign Monitoring
- Type
- article
- Field-Weighted Citation Impact
- 3.87
Funders
- National Natural Science Foundation of China
- Basic and Applied Basic Research Foundation of Guangdong Province