RAPID: Electronic Tattoos for Detection of Pre-symptoms of Infection

德克萨斯A M大学（TAMU）和德克萨斯大学（UT）提出了一种智能和小型化的贴片，使用基于石墨烯的电子纹身（e-tattoos）和一套算法来提取核心体温，用于检测感染前症状，在理解和控制呼吸道和非呼吸道病毒感染（包括冠状病毒COVID-19）的传播方面具有重要的实用性。皮肤温度在检测感染前症状中起着重要作用。该项目提供了三个智力优势：1）它创建了一种新颖的结构，使用薄膜永磁或电流控制磁体将完全灵活的石墨烯电子纹身智能地连接到刚性印刷电路板，以避免断裂，并提高机械鲁棒性，以实现不显眼的皮肤温度传感。2)该项目还创建了机器学习和深度学习算法，这些算法利用从传感器获取的生理时间序列来预测核心体温，并将导致确定感染的前期症状，同时处理噪声数据，并使用去噪自动编码器和Meta学习的概念为每个人实现计算模型的个性化。3)该项目创建了各种技术来解决基于低功耗微控制器（MCU）深度学习的预测算法的实时操作，包括严格使用定点操作的方法。考虑到生理信号的缓慢变化率及其稀疏性，该项目将在各种时间尺度上利用差分传感。该项目的更广泛影响包括直接应对COVID-19大流行，旨在通过创建具有重要实用性的新型传感器来生成可操作的信息，从而保护医护人员和患者。此外，鉴于人们对可穿戴电子产品的兴趣日益浓厚，这种在非传统电子纹身平台上的软件、硬件和系统交叉点上的开创性研究工作可以在数据挖掘和智能传感器架构方面取得突破，用于移动的健康、健身和计算，从而实现更多的应用。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Texas A&M University (TAMU) and University of Texas (UT) proposes a smart and miniaturized patch using graphene-based electronic tattoos (e-tattoos) and a suite of algorithms to extract core body temperature to be used to detect pre-symptoms of infection, with significant utility in understanding and controlling the spread of respiratory and non-respiratory viral infection including coronavirus COVID-19. Skin temperature plays an important role in detecting pre-symptoms of infection. The project provides three intellectual merits: 1) It creates a novel structure that intelligently interfaces a fully flexible graphene-based e-tattoo to rigid printed circuit board using thin film permanent or current-controlled magnets to avoid breakage and for improved mechanical robustness for unobtrusive skin temperature sensing. 2) The project also creates machine learning and deep learning algorithms that leverage the physiological times-series acquired from sensors to predict the core body temperature and will lead to determining pre-symptoms of infection while handling noisy data and enabling personalization of the computational models for each individual using the concept of denoising autoencoders and meta learning. 3) The project creates various techniques to address the real-time operation of the proposed prediction algorithm based on deep learning on low power microcontrollers (MCUs) including methods that strictly use fixed point operations. Given the slow rate of change in the physiological signals and their sparsity, this project will leverage differential sensing over various time scales. These signals can be processed by simplified deep neural network architectures with reduced mathematical operations that facilitates running it on the MCUs for detection.The broader impact of this project includes a direct response to the COVID-19 pandemic, aiming at protecting healthcare workers and patients through creating novel sensors with significant utility to generate actionable information. Additionally, in light of the growing interest in wearable electronics, this pioneering research effort at the intersection of software, hardware and systems on unconventional e-tattoo platforms can result in breakthrough in data mining and intelligent sensor architecture for mobile health, fitness and computing enabling a larger number of applications. The proposed novel sensing paradigm will provide opportunities for semi-conductor companies to consider new market opportunities and manufacture billions of chips.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.