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• W3022325124 abstract "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." @default.
• W3022325124 created "2020-05-13" @default.
• W3022325124 creator A5055237181 @default.
• W3022325124 creator A5086074961 @default.
• W3022325124 date "2020-05-07" @default.
• W3022325124 modified "2023-09-27" @default.
• W3022325124 title "Real-Time Context-aware Detection of Unsafe Events in Robot-Assisted Surgery" @default.
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• W3022325124 hasPublicationYear "2020" @default.
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