Real-Time Context-Aware Detection of Unsafe Events in Robot-Assisted Surgery
Real-Time Context-Aware Detection of Unsafe Events in Robot-Assisted Surgery
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DOI：
10.1109/dsn48063.2020.00054
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
M. S. Yasar;H. Alemzadeh
中科院分区：
文献类型：
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作者：
M. S. Yasar;H. Alemzadeh
用于机器人手术的网络物理系统使微创手术具有更高的精度和更短的住院时间。然而，随着软件的复杂性和连接性的增加以及人类操作员在手术机器人的监督中的主要参与，在确保患者安全方面仍然存在重大挑战。本文提出了一种安全监测系统，该系统提供了外科医生正在执行的手术任务的知识，可以实时检测安全关键事件。我们的方法集成了一个手术手势分类器，推断的操作上下文的时间序列的运动学数据的机器人与库的错误的手势分类器，给定的手术手势可以检测到不安全的事件。我们使用来自两个手术平台的数据进行的实验表明，所提出的系统可以在1，693毫秒的平均反应时间窗口和0.88的F1分数内检测由意外或恶意故障引起的不安全事件，并且在57毫秒的平均反应时间窗口和0.76的F1分数内检测人为错误。
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.