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A Fiber-Driven Finite Element Model for Predicting Residual Limb Soft Tissue Deformation: Applications in Prosthetic
Ling Wang1,2,3, Ziyan Qiu1,2,3, Lei Tang1,2,3
1State Key Laboratory for Manufacturing System Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, 710054, ShaanXi, People's Republic of China.
Purpose:
Changes in residual limb volume and shape pose significant challenges in achieving and maintaining an accurate and comfortable fit for prosthetic socket. While numerous techniques for measuring residual limb volume have been proposed, their clinical application remains limited by insufficient resolution and the inability to perform in-socket measurements. To address this issue, this study develops a novel method for predicting residual limb soft tissue deformation to guide prosthetic socket design.
Methods:
A three-dimensional (3D) finite element (FE) model of the human thigh was developed to simulate the soft tissue deformation during daily activities, driven by muscle contraction to replicate natural biomechanics. The model included hard tissue and muscle components, with the muscle modeled as a structure of evenly distributed, contractile fibers that generate movement. Parameters controlling fiber contraction were iteratively adjusted to best match the calculated tissue deformation and that observed in physical muscle models.
Results:
The optimized FE model significantly improved the accuracy of predicting dynamic soft tissue deformation, with average errors of 0.83% and 1.86% for tissue expansion and contraction regions, respectively. For various gait patterns, the average differences in equivalent volume and cross-sectional area changes were also less than 0.83% and 1.86%, respectively.
Conclusion:
The model demonstrated consistent prediction accuracy across different gait data. The fiber-driven soft tissue model developed offers a valuable tool for pre-design simulations of prosthetic sockets and orthoses. It is equally applicable to other wearable devices that interface with the skin, providing a robust framework for improving device design and functionality.

