Literature DB >> 28660644

Motion control skill assessment based on kinematic analysis of robotic end-effector movements.

Ke Liang1, Yuan Xing2, Jianmin Li2, Shuxin Wang2, Aimin Li3, Jinhua Li2.   

Abstract

BACKGROUND: The performance of robotic end-effector movements can reflect the user's operation skill difference in robot-assisted minimally invasive surgery. This study quantified the trade-off of speed-accuracy-stability by kinematic analysis of robotic end-effector movements to assess the motion control skill of users with different levels of experience.
METHODS: Using 'MicroHand S' system, 10 experts, 10 residents and 10 novices performed single-hand test and bimanual coordination test. Eight metrics based on the movements of robotic end-effectors were applied to evaluate the users' performance.
RESULTS: In the single-hand test, experts outperformed other groups except for movement speed; in the bimanual coordination test, experts also performed better except for movement time and movement speed. No statistically significant difference in performance was found between residents and novices.
CONCLUSIONS: The kinematic differences obtained from the movements of robotic end-effectors can be applied to assess the motion control skill of users with different skill levels.
Copyright © 2017 John Wiley & Sons, Ltd.

Keywords:  evaluation model; motion control skill; robotic end-effector kinematic feature; surgical robot system

Mesh:

Year:  2017        PMID: 28660644     DOI: 10.1002/rcs.1845

Source DB:  PubMed          Journal:  Int J Med Robot        ISSN: 1478-5951            Impact factor:   2.547


  1 in total

Review 1.  Deep learning with convolutional neural network for objective skill evaluation in robot-assisted surgery.

Authors:  Ziheng Wang; Ann Majewicz Fey
Journal:  Int J Comput Assist Radiol Surg       Date:  2018-09-25       Impact factor: 2.924

  1 in total

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