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An improved method for soft tissue modeling

delete2021-03-01
delete136
PRE
AI
Y
Yushan Tang
S
Shan Liu
Y
Yaru Deng
Y
Yuhui Zhang
L
Lirong Yin
W
Wenfeng Zheng *
DOI:10.1016/j.bspc.2020.102367delete
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摘要

摘要

En 中文
The technique of force and haptic reappearance is an effective method to solve the shortage of haptic presence and improve the medical robots' practicability. Soft tissue models, the core of force-haptic reappearance systems, play a decisive role in its performance. The establishment of realistic soft tissue models can improve the system's authenticity and efficiency and better realize the representation of force and touch in the interaction process. At present, there exists a contradiction between timeliness and accuracy in soft tissue modeling. This paper combined the finite element method with the mass-spring model. We estimated the mass-spring model's parameters with the finite element method by neglecting the damping coefficient and obtained the relationship between the elastic coefficients. Then, according to the real measurement data of soft tissue in literature and the stress-strain curve obtained from real measurement, the values of a, A, epsilon, k(s) sigma were determined. Through those methods above, an improved soft tissue model was obtained. Through our comparison experiments, the improved spring particle model has a high degree of data fitting, and the force value under the same displacement is smaller. Moreover, the improved model's force-displacement curve in the large deformation stage is still very close to the measurement curve of the volume, which cannot be achieved by the empirical spring particle model. These comparative experiments show that the improved finite element-based particle spring model can better consider the timeliness and accuracy and simulate the soft tissue more accurately.
Keyword:
Force and haptic
Finite element method
Particle-Spring model
Soft tissue model
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期刊

Biomedical Signal Processing and Control 封面图
Biomedical Signal Processing and Control
IF:
4.9
论文数:
9.9K
被引数:
2.4W

机构

L
Louisiana State University
学者数:
9.8K
论文数: 8.0K
被引数: 1.6W
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