Evidence map›Paper›PMID 40685463›Full record

ArticleAnnals of biomedical engineering2025

Design of a Wearable Finger PPG-Based Blood Glucose Monitor.

Mutian Wang, Xuelei Liu, Wenyi Han, Xinyu Lin, Xin Chen, Shun Zhao, Zhiqiang Zhuang, Leian Zhang, Peiqiang Su

Abstract read
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Article in Annals of biomedical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Mutian WangSchool of Mechanical Engineering, Shandong University of Technology, Zibo, 255000, Shandong, China.
Xuelei LiuSchool of Mechanical Engineering, Shandong University of Technology, Zibo, 255000, Shandong, China. liuxl@sdut.edu.cn.ORCID http://orcid.org/0009-0006-0436-6423
Wenyi HanSchool of Mechanical Engineering, Shandong University of Technology, Zibo, 255000, Shandong, China.
Xinyu LinSchool of Mechanical Engineering, Shandong University of Technology, Zibo, 255000, Shandong, China.
Xin ChenSchool of Mechanical Engineering, Shandong University of Technology, Zibo, 255000, Shandong, China.
Shun ZhaoSchool of Mechanical Engineering, Shandong University of Technology, Zibo, 255000, Shandong, China.
Zhiqiang ZhuangSchool of Mechanical Engineering, Shandong University of Technology, Zibo, 255000, Shandong, China.
Leian ZhangSchool of Mechanical Engineering, Shandong University of Technology, Zibo, 255000, Shandong, China.
Peiqiang SuShandong Huiyu Auto Parts Co., Ltd., Binzhou, 251702, Shandong, China.

Funding

Natural Science Foundation of Shandong Province ZR2022QE289
6 · The paper itself

Abstract

purposeBlood glucose monitoring is crucial for controlling diabetes. However, traditional fingertip pricking methods usually cause discomfort to patients and cannot achieve continuous monitoring. To overcome these limitations, we developed a novel, non-invasive, and wearable device for continuous blood glucose levels (BGLs) monitoring.

methodsThe device is equipped with a pulse oximeter, which has a visible wavelength (660 nm) and an infrared wavelength (880 nm) light-emitting diode (LED) to acquire finger photoplethysmography (PPG). The BGLs from PPG were estimated using a multi-layer perceptron (MLP) machine learning model, which was trained on dual-wavelength PPG intensity pertaining to various BGLs. We also analyzed the effect of MLP training parameters on the accuracy of blood glucose prediction.

resultsExperimental results indicate that 99.33% of the BGLs estimated from PPG lie in the clinically acceptable Clarke error grid (CEG) regions A and B, suggesting a high potential for accurate blood glucose monitoring with minimal clinical risk. Additionally, our 24-hour monitoring test further validates the device's capability to effectively track daily glucose fluctuations, which verifies its reliability in daily blood glucose monitoring.

conclusionIn conclusion, our novel wearable device for continuous blood glucose monitoring has shown feasibility and effectiveness. By leveraging PPG signals and a machine learning model, we have developed a promising alternative to traditional invasive blood glucose monitoring methods. This device has the potential to significantly improve the quality of life for diabetes patients by providing a more comfortable and continuous monitoring option.

Indexed as

Blood GlucoseBlood Glucose Self-MonitoringFingersPhotoplethysmographyWearable Electronic DevicesAdultEquipment DesignFemaleHumansMachine LearningMaleBlood GlucoseContinuous blood glucose monitoringFinger photoplethysmography (PPG)Machine learningMulti-layer perceptron (MLP)Wearable device

Identifiers

PMID40685463

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.