Development of an Intuitive Foot-Machine Interface for Robotic Surgery

TL;DR

This paper presents a novel passive foot-machine interface for robotic surgery that enables intuitive control of a robotic arm, enhancing surgical dexterity and independence from assistants.

Contribution

It introduces a new passive haptic foot-machine interface with a hybrid structure and ICA-based mapping, demonstrating improved control accuracy and adaptability.

Findings

ICA mapping outperforms other models in accuracy

System allows precise, adaptive foot control of robotic arm

Validated with experiments on 10 subjects

Abstract

The human-machine interface is of critical importance for master-slave control of the robotic system for surgery, in which current systems offer the control or two robotic arms teleoperated by the surgeon's hands. To relax the need for surgical assistants and augment dexterity in surgery, it has been recently proposed to use a robot like a third arm that can be controlled seamlessly, independently from the natural arms, and work together with them. This report will develop and investigate this concept by implementing foot control of a robotic surgical arm. A novel passive haptic foot-machine interface system and analysis of its performances were introduced in this report. This interface using a parallel-serial hybrid structure with springs and force sensors, which allows intuitive control of a slave robotic arm with four degrees of freedom (dof). The elastic isometric design enables a user to control the interface system accurately and adaptively, with an enlarged sensing range breaking the physical restriction of the pedal size. A subject specific (independent component analysis, ICA) model is identified to map the surgeon's foot movements into kinematic parameters of the slave robotic arm. To validate the system and assess the performance it allows, 10 subjects carried out experiments to manipulate the foot-machine interface system in various movements. With these experimental data, the mapping models were built and verified. A comparison between different mapping models was made and analyzed proving the ICA algorithm is obviously dominant over other methods.