  Robot-assisted minimally invasive surgery is improving surgeon performance
and patient outcomes. This innovation is also turning what has been a
subjective practice into motion sequences that can be precisely measured. A
growing number of studies have used machine learning to analyze video and
kinematic data captured from surgical robots. In these studies, models are
typically trained on benchmark datasets for representative surgical tasks to
assess surgeon skill levels. While they have shown that novices and experts can
be accurately classified, it is not clear whether machine learning can separate
highly proficient surgeons from one another, especially without video data. In
this study, we explore the possibility of using only kinematic data to predict
surgeons of similar skill levels. We focus on a new dataset created from
surgical exercises on a simulation device for skill training. A simple,
efficient encoding scheme was devised to encode kinematic sequences so that
they were amenable to edge learning. We report that it is possible to identify
surgical fellows receiving near perfect scores in the simulation exercises
based on their motion characteristics alone. Further, our model could be
converted to a spiking neural network to train and infer on the Nengo
simulation framework with no loss in accuracy. Overall, this study suggests
that building neuromorphic models from sparse motion features may be a
potentially useful strategy for identifying surgeons and gestures with chips
deployed on robotic systems to offer adaptive assistance during surgery and
training with additional latency and privacy benefits.
