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