Evidence map›Paper›PMID 29594854›Full record

ArticleInternational journal of computer assisted radiology and surgery2018

Blended shared control utilizing online identification : Regulating grasping forces of a surrogate surgical grasper.

Trevor K Stephens, Nathan J Kong, Rodney L Dockter, John J O'Neill, Robert M Sweet, Timothy M Kowalewski

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Article in International journal of computer assisted radiology and surgery, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Conditions for reliable grip force and jaw angle estimation of da Vinci surgical tools.International journal of computer assisted radiology and surgery · 2019
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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Trevor K StephensDepartment of Mechanical Engineering, University of Minnesota, Minneapolis, MN, USA. steph594@umn.edu.ORCID http://orcid.org/0000-0001-9974-4090
Nathan J KongDepartment of Mechanical Engineering, University of Minnesota, Minneapolis, MN, USA.
Rodney L DockterDepartment of Mechanical Engineering, University of Minnesota, Minneapolis, MN, USA.
John J O'NeillDepartment of Mechanical Engineering, University of Minnesota, Minneapolis, MN, USA.
Robert M SweetDepartment of Urology, University of Washington, Seattle, WA, USA.
Timothy M KowalewskiDepartment of Mechanical Engineering, University of Minnesota, Minneapolis, MN, USA.

Funding

National Science Foundation 00039202
6 · The paper itself

Abstract

purposeSurgical robots are increasingly common, yet routine tasks such as tissue grasping remain potentially harmful with high occurrences of tissue crush injury due to the lack of force feedback from the grasper. This work aims to investigate whether a blended shared control framework which utilizes real-time identification of the object being grasped as part of the feedback may help address the prevalence of tissue crush injury in robotic surgeries.

methodsThis work tests the proposed shared control framework and tissue identification algorithm on a custom surrogate surgical robotic grasping setup. This scheme utilizes identification of the object being grasped as part of the feedback to regulate to a desired force. The blended shared control is arbitrated between human and an implicit force controller based on a computed confidence in the identification of the grasped object. The online identification is performed using least squares based on a nonlinear tissue model. Testing was performed on five silicone tissue surrogates. Twenty grasps were conducted, with half of the grasps performed under manual control and half of the grasps performed with the proposed blended shared control, to test the efficacy of the control scheme.

resultsThe identification method resulted in an average of 95% accuracy across all time samples of all tissue grasps using a full leave-grasp-out cross-validation. There was an average convergence time of [Formula: see text] ms across all training grasps for all tissue surrogates. Additionally, there was a reduction in peak forces induced during grasping for all tissue surrogates when applying blended shared control online.

conclusionThe blended shared control using online identification more successfully regulated grasping forces to the desired target force when compared with manual control. The preliminary work on this surrogate setup for surgical grasping merits further investigation on real surgical tools and with real human tissues.

Indexed as

AlgorithmsFeedbackHand StrengthHumansOnline SystemsRobotic Surgical ProceduresRobotic surgeryShared controlTissue crush injuryTissue identification

Identifiers

PMID29594854

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.