  Despite significant developments in the design of surgical robots and
automated techniques for objective evaluation of surgical skills, there are
still challenges in ensuring safety in robot-assisted minimally-invasive
surgery (RMIS). This paper presents a runtime monitoring system for the
detection of executional errors during surgical tasks through the analysis of
kinematic data. The proposed system incorporates dual Siamese neural networks
and knowledge of surgical context, including surgical tasks and gestures, their
distributional similarities, and common error modes, to learn the differences
between normal and erroneous surgical trajectories from small training
datasets. We evaluate the performance of the error detection using Siamese
networks compared to single CNN and LSTM networks trained with different levels
of contextual knowledge and training data, using the dry-lab demonstrations of
the Suturing and Needle Passing tasks from the JIGSAWS dataset. Our results
show that gesture specific task nonspecific Siamese networks obtain micro F1
scores of 0.94 (Siamese-CNN) and 0.95 (Siamese-LSTM), and perform better than
single CNN (0.86) and LSTM (0.87) networks. These Siamese networks also
outperform gesture nonspecific task specific Siamese-CNN and Siamese-LSTM
models for Suturing and Needle Passing.
