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Cited by 38Non-Invasive Vital Sign MonitoringSpectroscopy Techniques in Biomedical and Chemical ResearchPerovskite Materials and Applications

Highly Sensitive Perovskite Photoplethysmography Sensor for Blood Glucose Sensing Using Machine Learning Techniques

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Zheng, Yongjian, Zhan, Zhenye, Chen, Qiulan, Chen, Jianxin, Luo, Jianwen, Cai, Juntao, Zhou, Yang, Chen, Ke +1 more·2024·Advanced Science
DOI 10.1002/advs.202405681PMC PMC11578317
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Abstract

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.

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  • With the rapid development of AI and IoT technologies, the efficient data acquisition and processing capabilities of perovskite sensors provide reliable technical support for smart devices in fields such as smart healthcare and smart homes, driving innovation and development in related industries.

    “With the rapid development of AI and IoT technologies, the efficient data acquisition and processing capabilities of perovskite sensors provide reliable technical support for smart devices in fields such as smart healthcare and smart homes, driving innovation and development in related industries [134].”

    From Design Strategies and Emerging Applications of Perovskite-Based Sensors (2025) · cites this paper as [134]

    The Introduction and Results describe a perovskite sensor that acquires PPG signals and uses machine learning to estimate blood glucose; the Discussion identifies potential for medical monitoring and future commercialization. The paper does not address IoT, smart homes, or innovation across related industries, so the broader claim exceeds its findings. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • A near-infrared photoplethysmography (PPG) sensor was prepared using a vapor-deposited mixed tin-lead hybrid perovskite photodetector.

    “As illustrated in Figure 6C, a near-infrared photoplethysmography (PPG) sensor was prepared using a vapordeposited mixed tin-lead hybrid perovskite photodetector [134].”

    From Design Strategies and Emerging Applications of Perovskite-Based Sensors (2025) · cites this paper as [134]

  • The device demonstrated a high detectivity of 5.32 × 10^12 Jones and an impressive linear dynamic range of 204 dB under near-infrared light, enabling accurate extraction of 11 features from the PPG signal.

    “The device demonstrated a high detectivity of 5.32 × 10 12 Jones and an impressive linear dynamic range of 204 dB under near-infrared light, enabling accurate extraction of 11 features from the PPG signal techniques.”

    From Design Strategies and Emerging Applications of Perovskite-Based Sensors (2025) · cites this paper as [134]

  • Zheng et al. developed a near-infrared PPG sensor using a perovskite photodetector, achieving high detectivity (5.32 × 10^12 Jones) and a large dynamic range (204 dB).

    “Zheng et al. [134] developed a near-infrared PPG sensor using a perovskite photodetector, achieving high detectivity (5.32 × 10 12 Jones) and a large dynamic range (204 dB).”

    From Design Strategies and Emerging Applications of Perovskite-Based Sensors (2025) · cites this paper as [134]

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Cited by

Sorted by citations

Authors
Zheng, Y., Zhan, Z., Chen, Q., Chen, J., Luo, J., Cai, J., Zhou, Y., Chen, K., & Xie, W.
Affiliations
  • Jinan University (CN)
  • Guangdong Food and Drug Vocational College (CN)
  • First Affiliated Hospital of Jinan University (CN)
  • Guangzhou Research Institute of Optical Mechanical and Electronical Technologies Co.,Ltd Guangzhou Guangdong 510663 China
Corresponding author
Xie, Weiguang
ORCID
Venue
Advanced Science
Publisher
Wiley
Published
2024-09-20
Volume
11
Issue
43
Pages
e2405681-e2405681
ISSN
2198-3844 (ISSN-L)
DOI
10.1002/advs.202405681
PMC
PMC11578317
OpenAlex
W4402713265
Cited by
38
References
2
Citations by year
2026: 21 · 2025: 15 · 2024: 2
FWCI
4.17
Type
journal article
Language
en
Open access
Licence
Locations
  • Advanced Science (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Basic and Applied Basic Research Foundation of Guangdong Province (2019B151502049)
  • National Natural Science Foundation of China (62174072)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Spectroscopy Techniques in Biomedical and Chemical Research
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Perovskite Materials and Applications
Keywords
photoplethysmography, blood glucose monitoring, perovskite photodetectors, near-infrared sensing, non-invasive glucose monitoring, diabetes mellitus, self-powered sensors, early detection of diabetes, detectivity, linear dynamic range
MeSH terms
Machine Learning, Blood Glucose, Diabetes Mellitus, Equipment Design, Humans, Oxides, Titanium, Photoplethysmography, Calcium Compounds

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