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

Abstract 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.