research-article

Forecasting and simulation of cutting force in virtual surgery based on particle filtering

Authors:

Qiangqiang Cheng,

Chunsheng Yang,

Peter Xiaoping LiuAuthors Info & Claims

Applied Intelligence, Volume 51, Issue 4

Pages 1934 - 1946

https://doi.org/10.1007/s10489-020-01910-1

Published: 01 April 2021 Publication History

Abstract

An accurate and realistic force feedback is very important in determining the realism of virtual surgery. In order to improve the accuracy of force simulation in cutting procedures, we proposed a novel method for forecasting and simulating the cutting force based on a particle filtering (PF) technique. Since the probability density function (PDF) is represented by particles, it is able to estimate accurately the interaction force between the surgical tool and soft tissue during cutting processes. The root mean square error (RMSE) of the PF-based method ranges from 0.0014 to 0.0034, and the mean absolute error (MAE) is less than 0.0399. Comparison of the experiment results with other methods demonstrated that the PF-based method can achieve a higher accuracy with different cutting speeds and angles. The application of the PF-based method to a virtual liver cutting procedure confirmed the effectiveness and accuracy of this method.

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Cited By

Yao LDing WHe TLiu SNie L(2022)A multiobjective prediction model with incremental learning ability by developing a multi-source filter neural network for the electrolytic aluminium processApplied Intelligence10.1007/s10489-022-03314-952:15(17387-17409)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s10489-022-03314-9

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cover image Applied Intelligence

Applied Intelligence Volume 51, Issue 4

Apr 2021

874 pages

ISSN:0924-669X

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© Springer Science+Business Media, LLC, part of Springer Nature 2020.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 April 2021

Accepted: 25 August 2020

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National Natural Science Foundation of China
Natural Science Foundation of Jiangxi
Chinese Aeronautical Establishment

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Cited By

Yao LDing WHe TLiu SNie L(2022)A multiobjective prediction model with incremental learning ability by developing a multi-source filter neural network for the electrolytic aluminium processApplied Intelligence10.1007/s10489-022-03314-952:15(17387-17409)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s10489-022-03314-9

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