CPS: Small: Inkjet Printed Flexible Electronic CPS with Context-aware Events of Interest Detection

该项目将开发使用低成本喷墨印刷技术在薄膜聚合物上制造的新型、体戴式、柔性传感器，开发能够自动检测不同背景下的健康事件的新型算法，并通过分析传感器和环境数据开发新型数据可靠性指标实时。该项目将产生实践组件，用于在心脏病患者的临床环境中进行测试和验证，以确定其监测心脏状况的有效性。如果成功，该项目将为患者和临床医生提供一种工具，以改善自然环境中的健康监测。该研究预计将影响增材制造方法，柔性电子产品，健康监测以及智能和互联社区计划。它还将为本科生和研究生提供培训，并通过为高中生举办的暑期代码夏令营，让下一代学者和工作者接触这些技术。下一代网络物理系统（CPS）必须利用弹性和可靠的网络/物理接口，经济上可行，并能够自动可靠地处理超大数据。实现这一目标需要克服与计算和物理域的无缝集成以及可扩展CPS的多模态和多粒度数据的有意义解释相关的当前技术障碍。平衡理论与实验，该项目将：1）产生CPS接口的基础工程过程与薄膜柔性电子电极和低成本传感器制造的喷墨打印; 2）开发新的算法，用于自主处理传感器数据，以检测感兴趣的上下文感知事件和数据可靠性度量闭环CPS使用实时机器学习在边缘实现;以及3）在一项实际的试点研究中部署CPS实践组件，以探索心脏事件的检测和探索各种闭环反馈方法。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。

This project will develop novel, body-worn, flexible sensors fabricated using low-cost inkjet printing technology on thin film polymers, develop novel algorithms capable of automatically detecting health events in different contexts, and develop a novel data reliability metric by analyzing sensor and context data in real-time. The project will produce practice components for test and validation in a clinical setting with cardiac patients to determine their effectiveness for monitoring heart conditions. If successful, the project will provide patients and clinicians with a tool to improve health monitoring in natural environments. The research is expected to impact additive manufacturing methods, flexible electronics, health monitoring, and smart and connected communities initiatives. It will also provide training for undergraduate and graduate students and expose the next generation of scholars and workers to these technologies through a Summer Code Camp for high school students.Next generation Cyber-Physical Systems (CPS) must utilize resilient and reliable cyber/physical interfacing, be economically viable, and be capable of processing extremely large data automatically and reliably. Achieving this requires overcoming current technological barriers associated with seamless integration of computation and physical domains and meaningful interpretation of multimodal and multigrain data of scalable CPS. Balancing theory with experimentation, this project will: 1) produce foundational engineering process for CPS interface with thin-film flexible electronic electrodes and low cost sensors fabricated with inkjet printing; 2) develop new algorithms for autonomous processing of sensor data to detect context-aware events of interest and data reliability metric for closed-loop CPS using real-time machine learning implemented at edge; and 3) deploy CPS practice components in a real-life pilot study to explore detection of cardiac episodes and explore various closed loop feedback approaches.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.