深度学习辅助的仙人掌启发的透-生物安全的丰富补丁-隔离式可穿戴的汗水代谢评估
Yuwen Yan1, Ting Xiao1, Miaorong Lin1
1Su Bingtian Center for Speed Research and Training, Guangdong Provincial Key Laboratory of Speed Capability Research, College of Chemistry and Materials Science, School of Physical Education, Jinan University, Guangzhou 510632, China.
Biosensors
|December 24, 2025
概括
这项研究引入了一种新的生物传感器补丁,可以非侵入性地收集和分析汗水生物标志物. 以仙人掌为灵感的膜通过防止皮肤接触来确保安全,而深度学习可以准确地检测健康监测的生物标志物.
科学领域:
- 生物仿真工程 生物仿真工程
- 生物感应技术技术的生物感应技术
- 可穿戴的健康设备
背景情况:
- 可穿戴的汗水传感器提供非侵入性健康监测,但面临着皮肤污染和生物安全方面的挑战.
- 与传感器化学物质的直接皮肤接触可能会引起过敏反应,需要改进样本采集和隔离方法.
研究的目的:
- 开发一个安全高效的可穿戴汗水生物传感系统,使用仿生膜和深度学习.
- 为了解决直接皮肤接触的局限性,并确保可靠的汗水生物标志物分析,用于临床应用.
主要方法:
- 一个以仙人掌为灵感的简斯膜被设计为单向的汗水传输和通过拉普拉斯压力进行丰富.
- 一个深度学习辅助的光传感器,利用卷积神经网络 (CNN),被开发用于生物标志物检测.
- 一个包含4500个光图像的数据集被用来训练和评估机器学习算法.
主要成果:
- 简斯膜有效地在现场收集汗水,同时防止表皮污染并确保生物安全.
- 该CNN模型实现了氨基酸混合物,Ca2+和Cl-度的100%分类准确性.
- DL辅助光方法显示高一致性 (91.4-96.0%) 与光光谱学用于实际的汗水分析.
结论:
- 开发的生物仿真传感贴片为收集和分析汗液提供了一种可靠和安全的方法.
- 这项技术为非侵入性临床健康监测,早期疾病检测和诊断提供了一个实用的工具.
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