论文标题
在机器人辅助手术中对不安全事件的实时环境感知检测
Real-Time Context-aware Detection of Unsafe Events in Robot-Assisted Surgery
论文作者
论文摘要
用于机器人手术的网络物理系统已经实现了最小的侵入性手术，精确度和较短的住院治疗。但是，随着软件的复杂性和连通性的提高以及人类操作员参与外科机器人的监督，在确保患者安全方面仍然存在重大挑战。本文提出了一个安全监测系统，鉴于外科医生正在执行的手术任务的了解，可以实时检测至关重要的事件。我们的方法集成了一种手术手势分类器，该手术分类器从机器人的时间序列运动学数据中渗透了操作上下文，并与一个错误的手势分类器库相结合，该库给定手术手势的错误手势分类器库可以检测不安全的事件。我们使用来自两个手术平台数据的数据进行的实验表明，该系统可以在平均反应时间窗口内检测到由意外或恶意断层引起的不安全事件，而在57毫秒的平均反应时间窗口内，在平均反应时间窗口内，F1分数为0.88，人为错误，F1得分为0.76。
Cyber-physical systems for robotic surgery have enabled minimally invasive procedures with increased precision and shorter hospitalization. However, with increasing complexity and connectivity of software and major involvement of human operators in the supervision of surgical robots, there remain significant challenges in ensuring patient safety. This paper presents a safety monitoring system that, given the knowledge of the surgical task being performed by the surgeon, can detect safety-critical events in real-time. Our approach integrates a surgical gesture classifier that infers the operational context from the time-series kinematics data of the robot with a library of erroneous gesture classifiers that given a surgical gesture can detect unsafe events. Our experiments using data from two surgical platforms show that the proposed system can detect unsafe events caused by accidental or malicious faults within an average reaction time window of 1,693 milliseconds and F1 score of 0.88 and human errors within an average reaction time window of 57 milliseconds and F1 score of 0.76.
