Evidence map›Paper›PMID 41944231›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

Shadow-Calibrated Stereo Vision for Colorimetric Sweat Analysis.

Ting Xiao, Yuwen Yan, Miaorong Lin, Jiahui Chen, Jianxin Meng, Xiang Cui, Peng Zhang, Fengyu Li

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Ting XiaoCollege of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Speed Capability Research, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou, China.
Yuwen YanCollege of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Speed Capability Research, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou, China.
Miaorong LinCollege of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Speed Capability Research, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou, China.
Jiahui ChenCollege of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Speed Capability Research, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou, China.
Jianxin MengCollege of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Speed Capability Research, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou, China.
Xiang CuiDepartment of Orthopedics, National Clinical Research Center for Orthopedics, Sports Medicine & Rehabilitation, Chinese PLA General Hospital, Beijing, China.
Peng ZhangCollege of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Speed Capability Research, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou, China.
Fengyu LiCollege of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Speed Capability Research, Su Bingtian Center for Speed Research and Training, Jinan University, Guangzhou, China.ORCID https://orcid.org/0000-0003-2481-6111

Funding

Guangdong Provincial Key Laboratory of Speed Capability Research 2023B1212010009Key Research and Development Project of Guangdong Province 2023A1515011031National Natural Science Foundation of China 22474049
6 · The paper itself

Abstract

The image recognition technologies, such as facial recognition and object detection, have found widespread application across diverse fields. However, conventional monocular camera systems capture only 2D information, rendering the accurate reconstruction of 3D morphological features challenging and thereby limiting their utility in precision measurement applications. To address this limitation, this study proposes a novel shadow-assisted 3D calibration methodology. This approach reconstructs 3D morphology from a monocular camera viewpoint by introducing a controllable light source and leveraging shadow geometry. In this work, we establish a mathematical model for calibrating 3D structures using shadows and employ Convolutional Neural Network-based 2D image analysis to achieve volumetric calibration. Applied to 3D hydrogel swelling colorimetric analysis for biomarker concentration determination, this method achieves a coefficient of determination (R

Indexed as

ColorimetryImage Processing, Computer-AssistedImaging, Three-DimensionalSweatCalibrationConvolutional Neural NetworksHumanscolorimetric analysisdeep‐learningshadow‐calibratedstereo visionsweat analysis

Identifiers

PMID41944231
PMCPMC13317592

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.