Sensorless Real-Time Force Estimation in Microsurgery Robots Using a Time Series Convolutional Neural Network

Robotic-assisted microsurgeries provide several benefits to both patients and surgeons. Nevertheless, there are still some limitations and challenges associated with their outcome, one of which is a lack of force feedback. Without force information, the risk of delicate tissue damage from the excess...

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Autores principales:	Jiuyun Xia, Kazuo Kiguchi
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Force estimation surgical tool visual information machine learning microsurgery Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/996e8a5f73a845beb069c1e33c739432
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id	oai:doaj.org-article:996e8a5f73a845beb069c1e33c739432
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spelling	oai:doaj.org-article:996e8a5f73a845beb069c1e33c7394322021-11-18T00:03:15ZSensorless Real-Time Force Estimation in Microsurgery Robots Using a Time Series Convolutional Neural Network2169-353610.1109/ACCESS.2021.3124304https://doaj.org/article/996e8a5f73a845beb069c1e33c7394322021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9594838/https://doaj.org/toc/2169-3536Robotic-assisted microsurgeries provide several benefits to both patients and surgeons. Nevertheless, there are still some limitations and challenges associated with their outcome, one of which is a lack of force feedback. Without force information, the risk of delicate tissue damage from the excessive force applied by surgeons would be increased. Since it is difficult to install force sensors on microsurgical tools, a novel approach for estimating a force vector from the deformation of the surgical tool is proposed in this paper. In the proposed approach, a surgical instrument that deforms according to the magnitude of the tool-to-tissue force is designed, and a time series convolution neural network is used to make the nonlinear relationship between the visual information of the deformation of the surgical tool and the applied forces in such a way that the tool-to-tissue force can be estimated according to the deformation of the surgical instrument in a real-time manner. The experimental results prove that the applied force can be successfully estimated with high accuracy in three dimensions using the proposed method.Jiuyun XiaKazuo KiguchiIEEEarticleForce estimationsurgical toolvisual informationmachine learningmicrosurgeryElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 149447-149455 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Force estimation surgical tool visual information machine learning microsurgery Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Force estimation surgical tool visual information machine learning microsurgery Electrical engineering. Electronics. Nuclear engineering TK1-9971 Jiuyun Xia Kazuo Kiguchi Sensorless Real-Time Force Estimation in Microsurgery Robots Using a Time Series Convolutional Neural Network
description	Robotic-assisted microsurgeries provide several benefits to both patients and surgeons. Nevertheless, there are still some limitations and challenges associated with their outcome, one of which is a lack of force feedback. Without force information, the risk of delicate tissue damage from the excessive force applied by surgeons would be increased. Since it is difficult to install force sensors on microsurgical tools, a novel approach for estimating a force vector from the deformation of the surgical tool is proposed in this paper. In the proposed approach, a surgical instrument that deforms according to the magnitude of the tool-to-tissue force is designed, and a time series convolution neural network is used to make the nonlinear relationship between the visual information of the deformation of the surgical tool and the applied forces in such a way that the tool-to-tissue force can be estimated according to the deformation of the surgical instrument in a real-time manner. The experimental results prove that the applied force can be successfully estimated with high accuracy in three dimensions using the proposed method.
format	article
author	Jiuyun Xia Kazuo Kiguchi
author_facet	Jiuyun Xia Kazuo Kiguchi
author_sort	Jiuyun Xia
title	Sensorless Real-Time Force Estimation in Microsurgery Robots Using a Time Series Convolutional Neural Network
title_short	Sensorless Real-Time Force Estimation in Microsurgery Robots Using a Time Series Convolutional Neural Network
title_full	Sensorless Real-Time Force Estimation in Microsurgery Robots Using a Time Series Convolutional Neural Network
title_fullStr	Sensorless Real-Time Force Estimation in Microsurgery Robots Using a Time Series Convolutional Neural Network
title_full_unstemmed	Sensorless Real-Time Force Estimation in Microsurgery Robots Using a Time Series Convolutional Neural Network
title_sort	sensorless real-time force estimation in microsurgery robots using a time series convolutional neural network
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/996e8a5f73a845beb069c1e33c739432
work_keys_str_mv	AT jiuyunxia sensorlessrealtimeforceestimationinmicrosurgeryrobotsusingatimeseriesconvolutionalneuralnetwork AT kazuokiguchi sensorlessrealtimeforceestimationinmicrosurgeryrobotsusingatimeseriesconvolutionalneuralnetwork
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Sensorless Real-Time Force Estimation in Microsurgery Robots Using a Time Series Convolutional Neural Network

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