  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.
