Nanomesh‐YOLO: Intelligent Colorimetry E‐Skin Based on Nanomesh and Deep Learning Object Detection Algorithm

Perspiration is an important physiological process that maintains thermal homeostasis and water–salt balance. However, the collection and analysis of perspiration currently rely on microfluidic technology and colorimetric assays. The complexity and high cost of fabricating microfluidic channels and...

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Veröffentlicht in:Advanced functional materials Jg. 34; H. 8
Hauptverfasser: Chen, Hongyu, Xu, Siye, Liu, Haidong, Liu, Chang, Liu, Houfang, Chen, Jiyang, Huang, Hexiang, Gong, Haoyu, Wu, Jingzhi, Tang, Hao, Luo, Jinan, Wen, Baohua, Zhou, Jianhua, Qiao, Yancong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken Wiley Subscription Services, Inc 01.02.2024
Schlagworte:
Algorithms
Capillarity
Chemical reactions
Colorimetry
colorimetry e‐skin
Deep learning
Finite element method
Homeostasis
Machine learning
Microfluidics
Object recognition
Perspiration
perspiration monitoring
Polyurethane resins
PU nanomesh
Reagents
Sweating
YOLOv3 object detection algorithm
ISSN:1616-301X, 1616-3028
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Zusammenfassung:Perspiration is an important physiological process that maintains thermal homeostasis and water–salt balance. However, the collection and analysis of perspiration currently rely on microfluidic technology and colorimetric assays. The complexity and high cost of fabricating microfluidic channels and the insecurity of chemical reagents for color reactions should be optimized. In this work, a colorimetry electronic skin (e‐skin) for intelligent perspiration monitoring has been realized. The colorimetry e‐skin system consists of the polyurethane (PU) nanomesh and the object detection algorithm You Only Look Once version 3 (YOLOv3). Due to the 44% porosity of the PU nanomesh and capillary action, the low‐cost PU nanomesh (<1 cent) can be used as the colorimetric indicator. The volume of the PU nanomesh expands to 362.37% as a result of perspiration being absorbed and changes the optical transmittance (up to 277.78%). A finite element model based on capillary action has been proposed to explain the change in optical transmittance. Finally, a database containing 735 images has been built, and the object detection algorithm YOLOv3 is used to analyze the perspiration absorbed by the PU nanomesh. The detection results can identify the perspiration volume with a high accuracy of 97%. These results show that this work has great potential in healthcare field. The PU nanomesh is prepared using electrospinning, which expands due to capillary action after absorbing perspiration, altering the optical transmittance and leading to color changes. After building the dataset of wet nanomesh, the object detection algorithm YOLOv3 is used to classify the perspiration volume to achieve the target detection and an intelligent early warning.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1616-301X
1616-3028
DOI:10.1002/adfm.202309798