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

职业：医疗人体网络物理系统中的上下文感知运行时安全保证

• 批准号: 2146295
• 负责人: Homa Alemzadeh
• 金额: $ 54.99万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-15 至 2027-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2146295&HistoricalAwards=false
• 关键词: CAREER; Context; Aware; Runtime; Safety

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.

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。医疗网络物理系统（MCPS）越来越依赖于复杂的连接软件和人工智能，用于各种诊断和治疗应用的控制和决策。但与其他CPS相反，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操作手术机器人是最复杂的人在回路MCPS的一个例子，设想在无法进入的地区，低资源人口和极端环境中实现远程操作。然而，越来越多的召回和不良事件报告表明，MCPS易受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。本研究的目的是探讨人在回路MCPS中运行时安全保证的基本问题，以及开发集成的模型和数据驱动功能，以便及时检测和缓解安全隐患，并降低对患者造成伤害的风险。拟议的研究将通过引入三个新颖的上下文设计原则，MCPS的感知安全引擎：（i）人类-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的正式规范和学习;（ii）通过多级监控和基于模型的状态估计对人类-网络-物理上下文进行实时推断，以检测即将发生的危险的可能性，时间和风险;（三）通过根据具体情况采取安全和纠正反应行动，防止在物理层产生不利后果，从而减少风险。研究活动将通过多学科课程开发和研究生实践培训，本科生的研究经验以及参与活动，包括暑期实习，女孩极客日，技术营地和K-12学生的黑客马拉松，与教育相结合。这些活动旨在扩大来自代表性不足的群体和少数民族的学生在CPS和医学研究工程中的参与，并培养下一代具有医疗保健，机器人，安全，该奖项反映了NSF的法定使命，并被认为是一项具有重大社会影响力的项目。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，

项目成果

Evaluating the Task Generalization of Temporal Convolutional Networks for Surgical Gesture and Motion Recognition Using Kinematic Data

• DOI: 10.1109/lra.2023.3292581
• 发表时间: 2023-06
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Kay Hutchinson;Ian Reyes;Zongyu Li;H. Alemzadeh

Towards Developing Safety Assurance Cases for Learning-Enabled Medical Cyber-Physical Systems

为学习型医疗网络物理系统开发安全保证案例

• 发表时间: 2023
• 期刊: Proceedings of the Workshop on Artificial Intelligence Safety 2023 (SafeAI 2023
• 作者: Maryam Bagheri;Josephine Lamp;Xugui Zhou;Lu Feng;Homa Alemzadeh
• 通讯作者: Homa Alemzadeh

Multimodal Transformers for Real-Time Surgical Activity Prediction

• DOI: 10.48550/arxiv.2403.06705
• 发表时间: 2024-03
• 期刊: ArXiv
• 作者: Keshara Weerasinghe;Seyed Hamid Reza Roodabeh;Kay Hutchinson;H. Alemzadeh

Towards Surgical Context Inference and Translation to Gestures

• DOI: 10.1109/icra48891.2023.10160383
• 发表时间: 2023-02
• 期刊: 2023 IEEE International Conference on Robotics and Automation (ICRA)
• 作者: Kay Hutchinson;Zongyu Li;Ian Reyes;H. Alemzadeh

Robotic Scene Segmentation with Memory Network for Runtime Surgical Context Inference

• DOI: 10.1109/iros55552.2023.10342013
• 发表时间: 2023-08
• 期刊: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: Zongyu Li;Ian Reyes;H. Alemzadeh