  Surgical skill directly affects surgical procedure outcomes; thus, effective
training is needed to ensure satisfactory results. Many objective assessment
metrics have been developed and some are widely used in surgical training
simulators. These objective metrics provide the trainee with descriptive
feedback about their performance however, often lack feedback on how to proceed
to improve performance. The most effective training method is one that is
intuitive, easy to understand, personalized to the user and provided in a
timely manner. We propose a framework to enable user-adaptive training using
near-real-time detection of performance, based on intuitive styles of surgical
movements (e.g., fluidity, smoothness, crispness, etc.), and propose a haptic
feedback framework to assist with correcting styles of movement. We evaluate
the ability of three types of force feedback (spring, damping, and spring plus
damping feedback), computed based on prior user positions, to improve different
stylistic behaviors of the user during kinematically constrained reaching
movement tasks. The results indicate that four out of the six styles studied
here were statistically significantly improved (p<0.05) using spring guidance
force feedback and a significant reduction in task time was also found using
spring feedback. The path straightness and targeting error in the task were
other task performance metrics studied which were improved significantly using
the spring-damping feedback. This study presents a groundwork for adaptive
training in robotic surgery based on near-real-time human-centric models of
surgical behavior.
