A Framework of State Estimation on Laminar Grinding Based on the CT Image-Force Model.

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In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

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5 · Who and what money

Authors and funding

3 authors.

Jihao LiuState Key Laboratory of Ocean Engineering, School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.ORCID 0000-0002-0639-5950

Guoyan ZhengInstitute of Medical Robotics, School of Medical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.ORCID 0000-0003-4173-0379

Weixin YanInstitute of Robotics, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Funding

National Science Foundation of China 62203295National Science Foundation of China U20A20199

6 · The paper itself

Abstract

It is a great challenge for a safe surgery to localize the cutting tip during laminar grinding. To address this problem, we develop a framework of state estimation based on the CT image-force model. For the proposed framework, the pre-operative CT image and intra-operative milling force signal work as source inputs. In the framework, a bone milling force prediction model is built, and the surgical planned paths can be transformed into the prediction sequences of milling force. The intra-operative milling force signal is segmented by the tumbling window algorithm. Then, the similarity between the prediction sequences and the segmented milling signal is derived by the dynamic time warping (DTW) algorithm. The derived similarity indicates the position of the cutting tip. Finally, to overcome influences of some factors, we used the random sample consensus (RANSAC). The code of the functional simulations has be opened.

Indexed as

CT imagemilling force predictionstate estimation

Identifiers

PMID39797030

DOI10.3390/s25010238

PMCPMC11723467

What Socratic holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

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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 Socratic graph.