Shared control of a medical robot with haptic guidance

Xiong, Linfei; Chng, Chin Boon; Chui, Chee-Kong; Yu, Peiwu; Li, Yao

doi:10.1007/s11548-016-1425-0

Cited by 33 publications

(37 citation statements)

References 27 publications

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“…In addition, the contextual visual approach could be used to extend the capabilities of haptic training procedures based on haptic guidance to provide information about the level of force to be applied during the execution of a specific task, such as the robotassisted system presented by Meli, Pacchierotti and Prattichizzo (2016) for training needle insertion and palpation tasks or the medical robot presented by Xiong et al (2016) used for minimally invasive surgery. Moreover, previous research has shown that the haptic feedback helps users to learn a trajectory (Feygin, Keehner and Tendick, 2002;Yang, Walter and Boulanger, 2008).…”

Section: Discussionmentioning

confidence: 99%

“…With regard to fine motor skills training (such as force exertion), Sewell et al (2007) proposed the use of haptic feedback for force skills transfer, and they analyzed the transference of force skills learned in a haptic-enabled virtual environment to performance on a surgically task (drilling a bone). Meli, Pacchierotti and Prattichizzo (2016) used haptic feedback to train a needle insertion and palpation task, and Xiong et al (2016) used force feedback for basic surgical skills training by using a medical robot.…”

Section: Accepted M M a N U mentioning

confidence: 99%

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Learning force patterns with a multimodal system using contextual cues

Rodríguez

Gutiérrez

Portillo

et al. 2018

International Journal of Human-Computer Studies

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This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.Highlights  A new interaction technique based on contextual information using a multimodal system for force pattern learning is proposed. Using the contextual information approach, the trainees obtain information about the evolution of their performance along the time (helping to correct the errors). The findings suggest that the contextual information provides a better trainee's performance than the punctual information. One drawback of the training strategies based on punctual feedback is the difficulty that users have in interpreting the information. The contextual approach could be used to extend the capabilities of haptic training procedures based on haptic guidance to provide information about the level of force to be applied during the execution of a specific task.Abstract -Previous studies on learning force patterns (fine motor skills) have focused on providing "punctual information", which means users only receive information about their performance at the current time step. This work proposes a new approach based on "contextual information", in which users receive information not only about the current time step, but also about the past (how the target force has changed over time) and the future (how the target force will change). A test was run to compare the performance of the contextual approach in relation to the punctual information, in which each participant had to memorize and then reproduce a pattern of force after training with a multimodal system. The findings suggest that the contextual approach is a useful strategy for force pattern learning. The advantage of the contextual information approach over the punctual information approach is that users receive information about the evolution of their performance (helping to correct the errors), and they also receive information about the next forces to be exerted (providing them with a better understanding of the target force profile). Finally, the contextual approach could be implemented in medical training platforms or surgical robots to extend the capabilities of these systems.

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Section: Discussionmentioning

confidence: 99%

Section: Accepted M M a N U mentioning

confidence: 99%

Learning force patterns with a multimodal system using contextual cues

Rodríguez

Gutiérrez

Portillo

et al. 2018

International Journal of Human-Computer Studies

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“…They were applied to teleoperated systems, for instance. Xiong et al [8] present a robotic mechanism dedicated to path-following and tumour-targeting tasks with a needle, in interventional radiology. They compute both a GVF and a FRVF in the form of a potential field to guide the user towards a path or a target and keep him/her away from simulated obstacles.…”

Section: Related Workmentioning

confidence: 99%

Design of haptic guides for pre-positioning assistance of a comanipulated needle

Gurnel

Marchal

Launay

et al. 2019

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)

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In minimally-invasive procedures like biopsy, the physician has to insert a needle into the tissues of a patient to reach a target. Currently, this task is mostly performed manually and under visual guidance. However, manual needle insertion can result in a large final positioning error of the tip that might lead to misdiagnosis and inadequate treatment. A way to solve this limitation is to use shared control; a gestureassistance paradigm that combines the cognitive skills of the operator with the precision, stamina and repeatability of a robotic or haptic device. In this paper, we propose to assist the physician with a haptic device that holds the needle and generates mechanical guides during the phase of manual needle pre-positioning. In the latter, the physician has to place the tip of the needle on a planned entry point, with a pre-defined angle of incidence. From this pre-operative information and also from intra-operative measurements, we propose to generate haptic cues, known as virtual fixtures, to guide the physician towards the desired position and orientation of the needle. It takes the form of five haptic guides, each one implementing virtual fixtures. We conducted a user study where those guides were compared to the unassisted reference gesture. The most constraining guide, in terms of assisted degrees of freedom, was highlighted as the one that provides the best results in terms of performance and user experience.

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“…Under the guidance of imaging technology or other detectors, flexible needles pass through soft tissue to complete MIS, administer local anesthetic drugs, sample blood, perform neurosurgery, achieve placement and brachytherapy, etc. [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ]. Meanwhile, percutaneous bioscopy and therapy are widely used in the prostate, lung, liver, kidney, spine, and other organs and tissues [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, needle insertion is the first step for most MIS, and the most fundamental manipulation during MIS [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. Physicians and patients anticipate a high-powered needle insertion surgery system with good reliability, high accuracy, rapid recovery, less pain, high satisfaction and low cost [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. The successful use of robotic-assisted operation systems such as da Vinci and ZEUS has attracted considerable interest from the medical and engineering communities [ 7 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Force Modeling, Identification, and Feedback Control of Robot-Assisted Needle Insertion: A Survey of the Literature

Yang

Xie

Liu

et al. 2018

Sensors

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Robot-assisted surgery is of growing interest in the surgical and engineering communities. The use of robots allows surgery to be performed with precision using smaller instruments and incisions, resulting in shorter healing times. However, using current technology, an operator cannot directly feel the operation because the surgeon-instrument and instrument-tissue interaction force feedbacks are lost during needle insertion. Advancements in force feedback and control not only help reduce tissue deformation and needle deflection but also provide the surgeon with better control over the surgical instruments. The goal of this review is to summarize the key components surrounding the force feedback and control during robot-assisted needle insertion. The literature search was conducted during the middle months of 2017 using mainstream academic search engines with a combination of keywords relevant to the field. In total, 166 articles with valuable contents were analyzed and grouped into five related topics. This survey systemically summarizes the state-of-the-art force control technologies for robot-assisted needle insertion, such as force modeling, measurement, the factors that influence the interaction force, parameter identification, and force control algorithms. All studies show force control is still at its initial stage. The influence factors, needle deflection or planning remain open for investigation in future.

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Shared control of a medical robot with haptic guidance

Cited by 33 publications

References 27 publications

Learning force patterns with a multimodal system using contextual cues

Learning force patterns with a multimodal system using contextual cues

Design of haptic guides for pre-positioning assistance of a comanipulated needle

Force Modeling, Identification, and Feedback Control of Robot-Assisted Needle Insertion: A Survey of the Literature

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