Analysing 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.
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 characterise erroneous parts of demonstrations by labelling video data, and use distribution similarity analysis and trajectory averaging on kinematic data to identify parameters that distinguish erroneous gestures.
Executional error frequency varies by task and gesture, and correlates with skill level. Some predominant error modes in each gesture are distinguishable by analysing error‐specific kinematic parameters. Procedural errors could lead to lower performance scores and increased demonstration times but also depend on surgical style.
This study provides insights into context‐dependent errors that can be used to design automated error detection mechanisms and improve training and skill assessment.
