Performance metrics for guidance active constraints in surgical robotics

Area of Science:

• Robotics in Surgery
• Human-Robot Interaction
• Surgical Skill Assessment

Background:

• Active Constraint (AC)/Virtual Fixture (VF) technologies are pivotal for shared control in robotic surgery.
• Evaluating ACs/VFs traditionally focuses on accuracy and completion time, often neglecting crucial human factors and user experience.
• The increasing implementation of ACs necessitates robust methods for assessing their safety and intuitiveness.

Purpose of the Study:

• To propose a comprehensive set of performance metrics for evaluating guidance ACs.
• To assess ACs considering accuracy enhancement, force characteristics, and subjective user aspects.
• To address the non-trivial assessment of human-robot interaction in shared surgical tasks.

Main Methods:

• Development of novel performance metrics and evaluation considerations for guidance ACs.
• Experimental validation using two distinct experimental setups.
• Inclusion of both expert surgeons (n=12) and inexperienced users (n=6) to capture diverse user perspectives.

Main Results:

• Demonstrated the utility of the proposed metrics in evaluating guidance ACs.
• Provided insights into accuracy enhancement, force feedback, and user subjective experience.
• Highlighted the importance of human factors in AC/VF system assessment.

Conclusions:

• The proposed metrics offer a more holistic approach to evaluating ACs/VFs in robotic surgery.
• Considering human factors and subjective aspects is crucial for optimizing user experience and safety.
• This framework aids in developing safer and more intuitive human-robot collaboration in surgical applications.