Dynamic Simulation of Joints Using Multi-Scale Modeling

该项目的目标是开发基于多尺度方法的有效神经网络模型，并展示这些模型在腿部肌肉骨骼模型中的实用性。这些模型将描述非线性，速率依赖，非均匀的半月板-胫骨-股骨关节的动态响应在计算效率模块化包。提出的研究将包括开发四种不同的增加尺度的计算模型，桥接各种尺度的新方法，以及通过动态膝关节加载机和临床步态实验室测量来验证模型。最终产品将是公开可用的神经网络模型，可以导出到商业集成开发环境或内部多体模型。这项工作将成为肌肉骨骼研究界的宝贵财富，为人类运动、假肢、组织工程、运动损伤和疾病等广泛领域的研究提供计算工具。

The objective of this project is to develop validated Neural Network models built upon a multi-scale approach and to demonstrate the utility of these models in a musculoskeletal model of the leg. These models will describe the non linear, rate dependent, non-homogenous dynamic response of menisci-tibio-femoral articulations in a computationally efficient modular package. The proposed research will include the development of four different computational models of increasing scale, novel methods to bridge the various scales, and model validation through a dynamic knee loading machine and clinical gait lab measurements. The final product will be publicly available Neural Network models that can be exported to commercial integrated development environments or in-house multi-body models. This work will be a valuable asset to the musculoskeletal research community providing computational tools that may aid research in broad areas such as human movement, prosthetics, tissue engineering, sport injury, and disease.