  Background Analyzing kinematic and video data can help identify potentially
erroneous motions that lead to sub-optimal surgeon performance and
safety-critical events in robot-assisted surgery.
  Methods We develop a rubric for identifying task and gesture-specific
Executional and Procedural errors and evaluate dry-lab demonstrations of
Suturing and Needle Passing tasks from the JIGSAWS dataset. We characterize
erroneous parts of demonstrations by labeling video data, and use distribution
similarity analysis and trajectory averaging on kinematic data to identify
parameters that distinguish erroneous gestures.
  Results Executional error frequency varies by task and gesture, and
correlates with skill level. Some predominant error modes in each gesture are
distinguishable by analyzing error-specific kinematic parameters. Procedural
errors could lead to lower performance scores and increased demonstration times
but also depend on surgical style.
  Conclusions This study provides insights into context-dependent errors that
can be used to design automated error detection mechanisms and improve training
and skill assessment.
