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

RAPID：用于检测感染前期症状的电子纹身

• 批准号: 2031674
• 负责人: Roozbeh Jafari
• 金额: $ 20万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2031674&HistoricalAwards=false
• 关键词: RAPID; Electronic; Tattoos; Detection; Pre

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.

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（电子纹身）和一套算法来提取核心体温，用于检测感染的前期症状，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作用。皮肤温度在检测感染的前期症状方面起着重要作用。该项目提供了三个智力优势：1)它创造了一种新颖的结构，可以使用薄膜永久或电流控制磁铁将完全柔性的石墨烯电子纹身智能地连接到刚性印刷电路板上，以避免破裂，并提高机械稳健性，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，利用从传感器获取的生理时间序列来预测核心体温，并在处理噪声数据的同时确定感染的前期症状，并使用去噪自动编码器和元学习的概念为每个人实现个性化的计算模型。3)该项目创建了各种技术来解决基于深度学习的预测算法在低功耗微控制器（mcu）上的实时操作，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，该项目将在不同的时间尺度上利用差分传感。这些信号可以通过简化的深度神经网络架构处理，减少了数学运算，便于在mcu上运行以进行检测。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效用的新型传感器来产生可操作的信息，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，这项开创性的研究工作将软件、硬件和系统结合在非传统的电子纹身平台上，这将为移动健康、健身和计算带来数据挖掘和智能传感器架构方面的突破，从而实现更多的应用。提出的新型传感范例将为半导体公司提供考虑新市场机会和制造数十亿芯片的机会。该奖项反映了美国国家科学基金会的法定使命，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。

项目成果

Fabrication, characterization and applications of graphene electronic tattoos

• DOI: 10.1038/s41596-020-00489-8
• 发表时间: 2021-04-12
• 期刊: NATURE PROTOCOLS
• 影响因子: 14.8
• 作者: Kireev, Dmitry;Ameri, Shideh Kabiri;Akinwande, Deji
• 通讯作者: Akinwande, Deji