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

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。医疗网络物理系统(MCPS)在各种诊断和治疗应用中越来越依赖复杂和互联的软件和人工智能来进行控制和决策。但与其他CP相反，它们往往依靠先进的医学知识和人类专业知识进行实时操作。用于微创手术的远程操作手术机器人是最复杂的人在环路MCP的一个例子，设想使远程手术能够在难以到达的地区、低资源人群和极端环境中进行。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，这可能会对患者产生负面影响。这项研究的目的是研究人在环路中的运行时安全保证的基本问题，并开发集成的模型和数据驱动的能力，以及时检测和缓解安全隐患，降低对患者的伤害风险。本研究将通过引入三个新的上下文感知安全引擎设计原则来促进运行时验证和异常检测的发展：(I)形式化描述和学习人-网络-物理系统上下文及其与导致危险和事故的潜在不安全控制行为的关系；(2)通过多层次监测和基于模型的状态估计，实时推断人-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；(3)通过环境感知产生安全和纠正的应对行动，在物理层防止不利后果，减少具有风险意识的灾害。研究活动将通过为研究生开发多学科课程和实践培训，为本科生提供研究体验，以及参与活动，包括暑期实习、女孩极客日、科技夏令营和K-12学生黑客松，与教育相结合。这些活动旨在扩大来自代表不足的群体和少数族裔的学生对CPS和医学研究工程的参与，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，这些领域具有潜在的重大社会影响。该提议由NSF CPS职业计划资助。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 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.