Adaptive Surgical Robotic Training Using Real-Time Stylistic Behavior Feedback Through Haptic Cues

TL;DR

This paper introduces a real-time, user-adaptive surgical training framework that uses stylistic movement feedback via haptic cues to improve surgical skills, demonstrating significant improvements in movement style and task efficiency.

Contribution

It presents a novel adaptive training system combining stylistic movement detection with haptic feedback to enhance surgical skill acquisition.

Findings

Spring guidance force improved stylistic behaviors significantly.

Spring feedback reduced task completion time.

Spring-damping feedback enhanced path straightness and targeting accuracy.

Abstract

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.