CAREER: Context-Aware Runtime Safety Assurance in Medical Human-Cyber-Physical Systems

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。医疗信息物理系统（MCPS）越来越依赖于复杂和连接的软件和人工智能来控制和决策各种诊断和治疗应用。但与其他CPS不同的是，它们往往依赖于先进的医学知识和人类的专业知识来进行实时操作。用于微创手术的远程手术机器人是最复杂的人在环MCPS的一个例子，设想在难以到达的地区、低资源人群和极端环境中实现远程手术。然而，越来越多的召回和不良事件报告表明，MCPS容易受到意外或恶意制造的故障和人为错误的影响，对患者有潜在的负面影响。本研究旨在探讨人在环MCPS运行时安全保障的根本问题，并开发集成模型和数据驱动能力，以及时发现和缓解安全隐患，降低对患者的伤害风险。提出的研究将通过引入MCPS环境感知安全引擎设计的三个新原则来推进运行时验证和异常检测方面的最新技术：(i)人-网络-物理系统环境的正式规范和学习，以及它们与导致危险和事故的潜在不安全控制行为的关系；（ii）通过多层次监测和基于模型的状态估计，实时推断人类-网络-物理环境，以检测即将发生的危险的可能性、时间和风险；㈢通过根据具体情况制定安全和纠正性应对行动，防止在物理层产生不利后果，从而认识到风险，减轻危害。研究活动将通过多学科课程开发和研究生实践培训、本科生研究经验以及参与活动（包括夏季实习、女孩极客日、技术营和K-12学生的黑客马拉松）与教育相结合。这些活动旨在扩大代表性不足的群体和少数民族学生在CPS和医学研究工程方面的参与，并培养下一代具有医疗保健、机器人、安全和安保等领域专业知识的多元化CPS专业人员，这些专业人员可能会产生重大的社会影响。该提案由NSF CPS CAREER项目资助。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。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Medical Cyber-Physical Systems (MCPS) increasingly rely on complex and connected software and artificial intelligence for control and decision making in various diagnostic and therapeutic applications. But contrary to other CPS, they often depend on advanced medical knowledge and human expertise for real-time operation. Tele-operated surgical robots for minimally invasive surgery are an example of the most complex human-in-the-loop MCPS, envisioned to enable remote operations in inaccessible areas, low-resource populations, and extreme environments. However, an increasing number of recalls and adverse event reports have shown the vulnerability of MCPS to accidental or maliciously-crafted faults and human errors with potential negative impacts on patients. The objective of this research is to investigate the fundamental problem of runtime safety assurance in human-in-the-loop MCPS, and to develop integrated model and data-driven capabilities for timely detection and mitigation of safety hazards and reducing the risk of harm to patients.The proposed research will advance the state-of-the-art in runtime verification and anomaly detection by introducing three novel principles for design of context-aware safety engines for MCPS: (i) Formal specification and learning of human-cyber-physical system contexts and their relationship with potentially unsafe control actions that lead to hazards and accidents; (ii) Real-time inference of the human-cyber-physical context through multi-level monitoring and model-based state estimation to detect the likelihood, timing, and risk of impending hazards; (iii) Risk-aware hazard mitigation through the context-aware generation of safe and corrective response actions that prevent adverse consequences in the physical layer. The research activities will be integrated with education through multi-disciplinary curriculum development and hands-on training for graduate students, research experience for undergraduates, and engagement activities, including summer internships, Girls’ Geek Days, tech camps, and hackathons for K-12 students. These activities aim to broaden the participation of students from underrepresented groups and minorities in CPS and engineering in medicine research and train the next generation of diverse CPS professionals with expertise in areas such as healthcare, robotics, safety, and security that have the potential for significant societal impact.This proposal was funded under the NSF CPS CAREER program.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.