Many biomedical, orthopaedic, and industrial applications are emerging that will benefit from personalized neuromusculoskeletal models. Applications include refined diagnostics, prediction of treatment trajectories for neuromusculoskeletal diseases, in silico design, development, and testing of medical implants, and human–machine interfaces to support assistive technologies. This review proposes how physics-based simulation, combined with machine learning approaches from big data, can be used to develop high-fidelity personalized representations of the human neuromusculoskeletal system. The core neuromusculoskeletal model features requiring personalization are identified, and big data/machine learning approaches for implementation are presented together with recommendations for further research.

中文翻译：

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支持个性化神经肌肉骨骼建模的机器学习方法。

新兴的许多生物医学，骨科和工业应用将受益于个性化的神经肌肉骨骼模型。应用包括完善的诊断，神经肌肉骨骼疾病的治疗轨迹预测，计算机植入物的计算机设计，开发和测试，以及支持辅助技术的人机界面。这篇评论提出了基于物理的模拟，再结合来自大数据的机器学习方法，可用于开发人类神经肌肉骨骼系统的高保真个性化表示。确定了需要个性化的核心神经肌肉骨骼模型特征，并提出了实现的大数据/机器学习方法以及进一步研究的建议。