PIDD-MSK: Physics-Informed Data-Driven Musculoskeletal Modelling

PIDD-MSK：物理信息数据驱动的肌肉骨骼建模

• 批准号: EP/Y027930/1
• 负责人: Zhiqiang Zhang
• 金额: $ 25.55万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY027930%2F1
• 关键词: PIDD; MSK; Physics; Informed; Data

Musculoskeletal models are powerful, computational simulation tools for detailed biomechanical analysis to estimate internal movement variables, which cannot be readily measured in vivo but are of interest to clinicians. The traditional physics-based musculoskeletal models are time-consuming and with high running latency, while the data-driven musculoskeletal models are fast but cannot reflect the underlying physical mechanisms.This proposal is to overcome limitations associated with both types of musculoskeletal models while preserving their advantages, and my vision is to develop the next-generation physics-informed data-driven musculoskeletal models, which can seamlessly integrate the existing physics-based domain knowledge into the data-driven models. The main innovations are 1) to bring physics information into data-driven musculoskeletal models to overcome its limitations by creating data reflecting the underlying physical mechanisms, and 2) to bring deep learning into physics-based musculoskeletal models to reduce computational demands in data processing and improve execution speed for real-time applications. I will apply the developed model for quantitative upper extremity impairment assessment for stroke patients as the case study. In addition to stroke patients, the outcome of this project can also potentially benefit millions of people with conditions such as multiple sclerosis and spinal cord injury, as well as people with musculoskeletal conditions.This fellowship would also improve my research skills, strengthen my academic profile, build my leadership capability and broaden my collaboration links in machine learning/deep learning for healthcare, and it will lay the foundation for me to become a worldleading expert in this area so that I can make scientific contributions and generate societal and economic impacts for Europe in the future.

肌肉骨骼模型是强大的计算模拟工具，用于详细的生物力学分析，以估计内部运动变量，该变量无法在体内轻易测量，但对临床医生感兴趣。传统的基于物理学的肌肉骨骼模型是耗时的，并且具有很高的延迟，而数据驱动的肌肉骨骼模型却很快，但不能反映出潜在的物理机制。该建议是克服与两种类型的肌肉骨骼模型相关的限制，同时保存其优势，而我的视觉效果是开发出的，而我的视觉效果是开发忙碌的数据，这些忙碌的数据是忙碌的数据，可划分的数据，以下数据效果，可划分的数据 - 脉络效果，并创建了忙碌的数据。将现有基于物理的域知识无缝整合到数据驱动的模型中。主要创新是1）将物理信息带入数据驱动的肌肉骨骼模型中，以通过创建反映基本物理机制的数据来克服其局限性，而2）将深度学习带入基于物理的肌肉骨骼模型中，以减少数据处理中的计算需求，并提高实时应用的执行速度。我将应用开发的模型作为案例研究的中风患者的定量上肢障碍评估。除了中风患者外，该项目的结果还可以使数百万具有多发性硬化和脊髓损伤等状况的人受益，以及患有肌肉骨骼状况的人，这将提高我的研究技能，增强我的研究能力，增强我的学术知名度，建立我的领导能力并建立我的领导能力并扩大我在机器学习/深度学习中的联系，以使我成为医疗保健和这个领域的研究，以使我成为这个世界的研究，以使我成为这个领域的基础，并且是我的基础，这是我的基础，而我的领导才能成为一个成立的基础。将来对欧洲产生社会和经济影响。