基于分形保形传感器的深度学习辅助无创皮肤组织温度分布和血流灌注率监测

IF 9.1 1区 化学 Q1 CHEMISTRY, ANALYTICAL
Yuxin Ouyang, Yanhui Feng, Yongzheng Han, Lin Qiu
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引用次数: 0

摘要

皮肤热物理特性是健康评估的关键,实时监测可以早期发现皮肤相关问题。采用柔性印刷电路技术制作了聚二甲基硅氧烷封装的Peano分形共形传感器,在保持完全一致性的前提下,精确测量皮肤组织温度分布和皮肤血液灌注率。二次谐波方法可以实现精确的热物理性质提取,而不需要高精度锁定放大器。循环热模型和多任务学习卷积神经网络(MTLCNN)有助于快速预测热物性,而热阻抗网络在测量过程中捕获温度分布。该传感器提供稳定的热导率和扩散率测量,在~ 57.3°弯曲角下具有6.6%的不确定性。MTLCNN模型的综合相关系数为0.9054,具有较好的回归性能。热物理特性、灌注速率和温度分布之间的相互作用支持人体的热平衡。这种方法为改进非侵入性健康监测和诊断提供了有价值的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Deep Learning–Aided Noninvasive Monitoring of Skin Tissue Temperature Distribution and Blood Perfusion Rate Based on Fractal Conformal Sensors

Deep Learning–Aided Noninvasive Monitoring of Skin Tissue Temperature Distribution and Blood Perfusion Rate Based on Fractal Conformal Sensors
Skin thermophysical properties are key for health assessment with real-time monitoring enabling early detection of skin-related issues. A polydimethylsiloxane-encapsulated Peano fractal conformal sensor is fabricated by flexible printed circuit technology to accurately measure skin tissue temperature distribution and dermal blood perfusion rate while maintaining full conformity. The second-harmonic method enables precise thermophysical property extraction without requiring high-precision lock-in amplifiers. A circular heat model and multitask learning convolutional neural network (MTLCNN) facilitate rapid thermophysical property prediction, while a thermal impedance network captures temperature distribution during measurements. The sensor provides stable measurements of thermal conductivity and diffusivity with 6.6% uncertainty at a ∼57.3° bending angle. The MTLCNN model achieves a combined correlation coefficient of 0.9054, demonstrating a superior regression performance. Interactions among thermophysical properties, perfusion rate, and temperature distribution support thermal balance in the human body. This approach offers valuable insights for improving noninvasive health monitoring and diagnostic.
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来源期刊
ACS Sensors
ACS Sensors Chemical Engineering-Bioengineering
CiteScore
14.50
自引率
3.40%
发文量
372
期刊介绍: ACS Sensors is a peer-reviewed research journal that focuses on the dissemination of new and original knowledge in the field of sensor science, particularly those that selectively sense chemical or biological species or processes. The journal covers a broad range of topics, including but not limited to biosensors, chemical sensors, gas sensors, intracellular sensors, single molecule sensors, cell chips, and microfluidic devices. It aims to publish articles that address conceptual advances in sensing technology applicable to various types of analytes or application papers that report on the use of existing sensing concepts in new ways or for new analytes.
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