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.

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.皮肤温度在检测预感染的症状中起着重要作用。该项目提供了三个智力优点：1）它创建了一种新颖的结构，该结构智能地将基于石墨烯的电子纹身连接到刚性印刷电路板上，使用薄膜永久性或电流控制的磁铁，以避免破裂，并改善机械鲁棒性，从而使机械鲁棒性不受欢迎。 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中获得的生理时间序列来预测核心体温，并将导致确定感染的预胞膜，同时处理嘈杂的数据并为每个人使用DeNoso deno outoencoders和Meta学习的概念为每个人提供计算模型的个性化。 3）该项目创建了各种技术，以基于对低功率微控制器（MCUS）的深度学习，包括严格使用固定点操作的方法，以解决所提出的预测算法的实时操作。鉴于生理信号及其稀疏性的变化速度缓慢，该项目将利用各种时间尺度的差异感。这些信号可以通过简化的深层神经网络体系结构进行处理，并减少了数学操作，从而有助于在MCUS上运行它以进行检测。该项目的更广泛影响包括对COVID-19-19的大流行的直接反应，旨在通过创建具有重大实用性的新型传感器来生成可行的信息，以保护医疗保健工人和患者。此外，鉴于对可穿戴电子产品的兴趣日益增长，这项在非常规的电子纹身平台上的软件，硬件和系统相交的开创性研究工作可能会导致数据挖掘的数据挖掘和智能传感器体系结构，以实现移动健康，健身和计算，从而实现更多应用。拟议中的新型传感范式将为半导体公司提供考虑新的市场机会并制造数十亿筹码的机会。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估评估的评估来审查标准的。

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