Head-mounted interface for intuitive vision control and continuous surgical operation in a surgical robot system.

Area of Science:

• Robotics
• Surgical Technology
• Human-Computer Interaction

Background:

• Robot-assisted surgery offers benefits but suffers from workflow interruptions due to control switching.
• Improving the efficiency of surgical robot systems is crucial for enhanced surgical outcomes.

Purpose of the Study:

• To develop and evaluate a head-mounted master interface (HMI) for continuous endoscopic control in robot-assisted surgery.
• To enable surgeons to manipulate the endoscopic robotic arm using head movements, thereby streamlining the surgical operation flow.

Main Methods:

• Implementation of a head-mounted master interface (HMI) integrated with a da Vinci Research Kit and a 4-DOF endoscope control system.
• Utilizing support vector machine (SVM) classification with ten-fold cross-validation to analyze head movement data from eight volunteers.
• Developing an endoscopic control algorithm based on the SVM classification results.

Main Results:

• Achieved a 92.28% classification accuracy using an SVM classifier with a Gaussian kernel (σ=0.85).
• Demonstrated a significant reduction in task completion time during a peg transfer task, indicating improved system usability.
• Measured a system time delay of 0.72 seconds.

Conclusions:

• The proposed head-mounted master interface (HMI) effectively enables continuous surgical operation flow in robot-assisted surgery.
• Head motion-controlled endoscopic manipulation improves surgical efficiency and reduces task completion time.
• The developed SVM-based control algorithm provides accurate and reliable classification of head movements for surgical robot control.