Tools for the Biomechanical Analysis of Human Movement: Application to Knee Osteoarthritis

人体运动生物力学分析工具：在膝骨关节炎中的应用

• 批准号: RGPIN-2015-04834
• 负责人: Deluzio, Kevin
• 金额: $ 3.13万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=688727
• 关键词: Tools; Biomechanical; Analysis; Human; Movement

My research career has focused on the development of engineering tools for the analysis of human movement. Working with my students, my research has contributed significantly to the development of techniques for the analysis and classification of human movement data. A challenge of biomechanical data is the substantial variability that may be confounded by uncertainties in the experimental methods. Ensuring reliable and consistent data within laboratories for longitudinal studies, between laboratories for data comparison, or in multicentre studies is, therefore, a significant and worthy challenge. I am proposing to improve the robustness and sensitivity of our classification techniques by quantifying and removing this nuisance variation. One of the key features of this new approach is that it can be applied to data already collected instead of only prescribing how data should be collected in the future.******We have applied our classification tools to knee joint function and its deterioration due to osteoarthritis (OA) as an exemplar mechanism and this has motivated our work in computational modelling. Biological joints, just like their mechanical counterparts, progressively deteriorate in response to excessive loading conditions. We cannot, however, measure internal knee loads or muscle forces without invasive and potentially confounding surgical procedures, Therefore, we rely on computational models to estimate these forces. Our research group and others, have obtained accurate joint contact force estimates for healthy gait. ******Modelling OA gait adds a substantial challenge that we propose to meet through an innovative combination of our classification techniques and musculoskeletal modelling. Muscle forces contribute greatly to joint contact forces, and the subjects with OA use their muscles differently from healthy subjects. To reflect this reality, the models must be informed with muscle activation patterns that are recorded using electromyogram (EMG). One way to do this is with an EMG-driven model where the EMG are used to actuate the muscles in a musculoskeletal model. However, EMG-driven models are limited due to, among other things, unsatisfactory solutions to the EMG-Force relation. We have developed an alternative approach, best characterized as "EMG-informed" optimization in which a subject-specific simulation is created, and then we perturb this simulation according to measured differences in EMG, based on our data analysis techniques. The value of this approach lies in our ability to create subject-specific, muscle activation patterns such as those observed in patients with musculoskeletal diseases like knee osteoarthritis. Our work could lead to important new tools for quantitative diagnosis, and evaluation of treatments. **

我的研究生涯主要集中在开发用于分析人体运动的工程工具。我与我的学生合作，研究为人类运动数据分析和分类技术的发展做出了重大贡献。生物力学数据的一个挑战是实验方法的不确定性可能会混淆巨大的变异性。因此，确保实验室内纵向研究、实验室之间数据比较或多中心研究的数据可靠且一致，是一项重大且值得挑战的挑战。我建议通过量化和消除这种令人讨厌的变化来提高我们分类技术的稳健性和敏感性。这种新方法的关键特征之一是它可以应用于已经收集的数据，而不仅仅是规定未来应如何收集数据。******我们已将我们的分类工具应用于膝关节功能及其骨关节炎（OA）引起的恶化作为一种​​典型机制，这激发了我们在计算建模方面的工作。生物关节，就像机械关节一样，会因过度负载条件而逐渐恶化。然而，我们无法在没有侵入性和潜在混淆的外科手术的情况下测量膝关节内部负荷或肌肉力，因此，我们依靠计算模型来估计这些力。我们的研究小组和其他人已经获得了健康步态的准确关节接触力估计。 ******OA 步态建模增加了一项重大挑战，我们建议通过分类技术和肌肉骨骼建模的创新组合来应对这一挑战。肌肉力量对关节接触力有很大贡献，患有骨关节炎的受试者使用肌肉的方式与健康受试者不同。为了反映这一现实，模型必须了解使用肌电图 (EMG) 记录的肌肉激活模式。实现此目的的一种方法是使用肌电图驱动模型，其中肌电图用于驱动肌肉骨骼模型中的肌肉。然而，肌电图驱动模型由于肌电图-力关系的解决方案不令人满意等原因而受到限制。我们开发了一种替代方法，最好的特点是“肌电图通知”优化，其中创建特定于主题的模拟，然后根据测量的肌电图差异，基于我们的数据分析技术，扰乱该模拟。这种方法的价值在于我们能够创建特定于受试者的肌肉激活模式，例如在患有膝骨关节炎等肌肉骨骼疾病的患者中观察到的模式。我们的工作可能会带来用于定量诊断和治疗评估的重要新工具。 **