Literature DB >> 24519031

Effects of robotic manipulators on movements of novices and surgeons.

Ilana Nisky1, Allison M Okamura, Michael H Hsieh.   

Abstract

BACKGROUND: Robot-assisted surgery is widely adopted for many procedures but has not realized its full potential to date. Based on human motor control theories, the authors hypothesized that the dynamics of the master manipulators impose challenges on the motor system of the user and may impair performance and slow down learning. Although studies have shown that robotic outcomes are correlated with the case experience of the surgeon, the relative contribution of cognitive versus motor skill is unknown. This study quantified the effects of da Vinci Si master manipulator dynamics on movements of novice users and experienced surgeons and suggests possible implications for training and robot design.
METHODS: In the reported study, six experienced robotic surgeons and ten novice nonmedical users performed movements under two conditions: teleoperation of a da Vinci Si Surgical system and freehand. A linear mixed model was applied to nine kinematic metrics (including endpoint error, movement time, peak speed, initial jerk, and deviation from a straight line) to assess the effects of teleoperation and expertise. To assess learning effects, t tests between the first and last movements of each type were used.
RESULTS: All the users moved slower during teleoperation than during freehand movements (F(1,9343) = 345; p < 0.001). The experienced surgeons had smaller errors than the novices (F(1,14) = 36.8; p < 0.001). The straightness of movements depended on their direction (F(7,9343) = 117; p < 0.001). Learning effects were observed in all conditions. Novice users first learned the task and then the dynamics of the manipulator.
CONCLUSIONS: The findings showed differences between the novices and the experienced surgeons for extremely simple point-to-point movements. The study demonstrated that manipulator dynamics affect user movements, suggesting that these dynamics could be improved in future robot designs. The authors showed the partial adaptation of novice users to the dynamics. Future studies are needed to evaluate whether it will be beneficial to include early training sessions dedicated to learning the dynamics of the manipulator.

Entities:  

Mesh:

Year:  2014        PMID: 24519031      PMCID: PMC8101070          DOI: 10.1007/s00464-014-3446-5

Source DB:  PubMed          Journal:  Surg Endosc        ISSN: 0930-2794            Impact factor:   4.584


  48 in total

1.  Robotic surgery and resident training.

Authors:  D A De Ugarte; D A Etzioni; C Gracia; J B Atkinson
Journal:  Surg Endosc       Date:  2003-03-28       Impact factor: 4.584

Review 2.  Robotic surgery: a current perspective.

Authors:  Anthony R Lanfranco; Andres E Castellanos; Jaydev P Desai; William C Meyers
Journal:  Ann Surg       Date:  2004-01       Impact factor: 12.969

3.  Towards automatic skill evaluation: detection and segmentation of robot-assisted surgical motions.

Authors:  Henry C Lin; Izhak Shafran; David Yuh; Gregory D Hager
Journal:  Comput Aided Surg       Date:  2006-09

4.  Tool-use induces morphological updating of the body schema.

Authors:  Lucilla Cardinali; Francesca Frassinetti; Claudio Brozzoli; Christian Urquizar; Alice C Roy; Alessandro Farnè
Journal:  Curr Biol       Date:  2009-06-23       Impact factor: 10.834

5.  Reach adaptation: what determines whether we learn an internal model of the tool or adapt the model of our arm?

Authors:  JoAnn Kluzik; Jörn Diedrichsen; Reza Shadmehr; Amy J Bastian
Journal:  J Neurophysiol       Date:  2008-07-02       Impact factor: 2.714

6.  Objective evaluation of expert and novice performance during robotic surgical training tasks.

Authors:  Timothy N Judkins; Dmitry Oleynikov; Nick Stergiou
Journal:  Surg Endosc       Date:  2008-04-29       Impact factor: 4.584

7.  Content and construct validation of a robotic surgery curriculum using an electromagnetic instrument tracker.

Authors:  Timothy J Tausch; Timothy M Kowalewski; Lee W White; Patrick S McDonough; Timothy C Brand; Thomas S Lendvay
Journal:  J Urol       Date:  2012-07-20       Impact factor: 7.450

Review 8.  Robotic surgical simulation.

Authors:  Michael A Liss; Elspeth M McDougall
Journal:  Cancer J       Date:  2013 Mar-Apr       Impact factor: 3.360

9.  The coordination of arm movements: an experimentally confirmed mathematical model.

Authors:  T Flash; N Hogan
Journal:  J Neurosci       Date:  1985-07       Impact factor: 6.167

10.  Robotic surgery training and performance: identifying objective variables for quantifying the extent of proficiency.

Authors:  K Narazaki; D Oleynikov; N Stergiou
Journal:  Surg Endosc       Date:  2005-12-07       Impact factor: 3.453

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  6 in total

1.  "Alarm-corrected" ergonomic armrest use could improve learning curves of novices on robotic simulator.

Authors:  Kun Yang; Manuela Perez; Gabriela Hossu; Nicolas Hubert; Cyril Perrenot; Jacques Hubert
Journal:  Surg Endosc       Date:  2016-05-17       Impact factor: 4.584

2.  Uncontrolled manifold analysis of arm joint angle variability during robotic teleoperation and freehand movement of surgeons and novices.

Authors:  Ilana Nisky; Michael H Hsieh; Allison M Okamura
Journal:  IEEE Trans Biomed Eng       Date:  2014-06-23       Impact factor: 4.538

3.  Robot-assisted surgery: an emerging platform for human neuroscience research.

Authors:  Anthony M Jarc; Ilana Nisky
Journal:  Front Hum Neurosci       Date:  2015-06-04       Impact factor: 3.169

4.  Human-centric predictive model of task difficulty for human-in-the-loop control tasks.

Authors:  Ziheng Wang; Ann Majewicz Fey
Journal:  PLoS One       Date:  2018-04-05       Impact factor: 3.240

5.  Surgeon-Centered Analysis of Robot-Assisted Needle Driving Under Different Force Feedback Conditions.

Authors:  Lidor Bahar; Yarden Sharon; Ilana Nisky
Journal:  Front Neurorobot       Date:  2020-01-24       Impact factor: 2.650

Review 6.  Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks.

Authors:  Koenraad Vandevoorde; Lukas Vollenkemper; Constanze Schwan; Martin Kohlhase; Wolfram Schenck
Journal:  Sensors (Basel)       Date:  2022-03-23       Impact factor: 3.576

  6 in total

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