Mapping Surgeon's Hand/Finger Motion During Conventional Microsurgery to Enhance Intuitive Surgical Robot Teleoperation

TL;DR

This study develops a wearable sensor system and deep learning approach to accurately map surgeon hand and finger movements to instrument poses, aiming to improve intuitive teleoperation in micro-surgery.

Contribution

It introduces a novel wearable motion tracking system combined with a deep neural network to estimate surgical tool poses from surgeon hand movements.

Findings

Wearable sensors did not interfere with surgery or instrument handling.

The deep neural network achieved less than 0.3% mean squared error in pose estimation.

The system enables more intuitive teleoperation for micro-surgery.

Abstract

Purpose: Recent developments in robotics and artificial intelligence (AI) have led to significant advances in healthcare technologies enhancing robot-assisted minimally invasive surgery (RAMIS) in some surgical specialties. However, current human-robot interfaces lack intuitive teleoperation and cannot mimic surgeon's hand/finger sensing and fine motion. These limitations make tele-operated robotic surgery not suitable for micro-surgery and difficult to learn for established surgeons. We report a pilot study showing an intuitive way of recording and mapping surgeon's gross hand motion and the fine synergic motion during cardiac micro-surgery as a way to enhance future intuitive teleoperation. Methods: We set to develop a prototype system able to train a Deep Neural Net-work (DNN) by mapping wrist, hand and surgical tool real-time data acquisition(RTDA) inputs during mock-up heart micro-surgery procedures. The trained network was used to estimate the tools poses from refined hand joint angles. Results: Based on surgeon's feedback during mock micro-surgery, the developed wearable system with light-weight sensors for motion tracking did not interfere with the surgery and instrument handling. The wearable motion tracking system used 15 finger-thumb-wrist joint angle sensors to generate meaningful data-sets representing inputs of the DNN network with new hand joint angles added as necessary based on comparing the estimated tool poses against measured tool pose. The DNN architecture was optimized for the highest estimation accuracy and the ability to determine the tool pose with the least mean squared error. This novel approach showed that the surgical instrument's pose, an essential requirement for teleoperation, can be accurately estimated from recorded surgeon's hand/finger movements with a mean squared error (MSE) less than 0.3%