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打印技术在薄膜聚合物上制造的新型、身体穿戴、灵活的传感器，开发能够在不同环境中自动检测健康事件的新算法，并通过实时分析传感器和环境数据开发新的数据可靠性指标。该项目将生产实践组件，用于在心脏病患者的临床环境中进行测试和验证，以确定他们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。这项研究预计将影响添加剂制造方法、灵活的电子产品、健康监测以及智能和互联社区的倡议。它还将为本科生和研究生提供培训，并通过面向高中生的夏令营让下一代学者和工作人员接触到这些技术。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，在经济上可行，并能够自动且可靠地处理超大数据。要做到这一点，需要克服目前与计算和物理领域的无缝结合有关的技术障碍，并对可伸缩的CP的多模式和多粒度数据进行有意义的解释。在理论与实验的平衡中，该项目将：1)为CPS接口产生基本的工程流程，使用薄膜柔性电子电极和用喷墨打印制造的低成本传感器；2)开发新的传感器数据自主处理算法，以检测感兴趣的上下文感知事件，并使用在EDGE上实施的实时机器学习为闭环系统CPS提供数据可靠性度量；以及3)在现实生活中部署CPS实践组件，以探索心脏发作的检测并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智力优势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.