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经验证的神经网络模型，并展示这些模型在腿部肌肉骨骼模型中的实用性。 这些模型将在计算效率高的组配式软件包中描述股骨-胫骨-股骨关节的非线性、频率依赖性、非均匀动态响应。 拟议的研究将包括开发四种不同的计算模型，增加规模，新的方法来桥接各种规模，并通过动态膝关节负荷机和临床步态实验室测量模型验证。最终产品将是公开的神经网络模型，可以导出到商业集成开发环境或内部多体模型。 这项工作将是一个宝贵的资产，肌肉骨骼研究界提供的计算工具，可以帮助研究在广泛的领域，如人体运动，假肢，组织工程，运动损伤和疾病。

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.