  Robotic-assisted minimally invasive surgery (MIS) has enabled procedures with
increased precision and dexterity, but surgical robots are still open loop and
require surgeons to work with a tele-operation console providing only limited
visual feedback. In this setting, mechanical failures, software faults, or
human errors might lead to adverse events resulting in patient complications or
fatalities. We argue that impending adverse events could be detected and
mitigated by applying context-specific safety constraints on the motions of the
robot. We present a context-aware safety monitoring system which segments a
surgical task into subtasks using kinematics data and monitors safety
constraints specific to each subtask. To test our hypothesis about context
specificity of safety constraints, we analyze recorded demonstrations of
dry-lab surgical tasks collected from the JIGSAWS database as well as from
experiments we conducted on a Raven II surgical robot. Analysis of the
trajectory data shows that each subtask of a given surgical procedure has
consistent safety constraints across multiple demonstrations by different
subjects. Our preliminary results show that violations of these safety
constraints lead to unsafe events, and there is often sufficient time between
the constraint violation and the safety-critical event to allow for a
corrective action.
