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: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=613195
• 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.

我的研究生涯集中在开发人类运动的工程工具上。与我的学生合作，我的研究为人类运动数据分析和分类的技术开发做出了重大贡献。生物力学数据的一个挑战是实质性变异性可能会被实验方法中的不确定性混淆。因此，确保实验室内的可靠和一致的数据进行纵向研究，实验室进行数据比较或在多中心研究中是一个重大而宝贵的挑战。我建议通过量化和消除这种滋扰变化来提高分类技术的鲁棒性和敏感性。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，而不仅仅是规定将来应该如何收集数据。 我们已经将分类工具应用于膝关节功能及其由于骨关节炎（OA）作为示例机制而引起的定义，这将我们的工作融合在计算建模中。生物关节就像它们的机械同步物一样，响应过度的负荷条件而逐渐侦探。但是，如果没有侵入性且潜在的手术程序，我们无法测量内部膝盖的负荷或肌肉力量，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联合接触力估计。 建模OA遇到了一个重大挑战，我们建议通过我们的分类技术和肌肉骨骼建模的创新组合来满足。肌肉力量对联合接触力有很大贡献，而OA的受试者使用其肌肉与健康受试者不同。为了反映这一现实，必须使用肌肉激活模式告知模型，这些肌肉激活模式使用肌电图（EMG）记录。一种方法是使用EMG驱动的模型，其中EMG用于激活肌肉骨骼模型中的肌肉。但是，由于对EMG-Force关系的解决方案不令人满意，因此EMG驱动的模型受到限制。我们开发了一种替代方法，最佳地表征为“ EMG信息”优化，在该优化中，创建了特定于主题的模拟，然后根据我们的数据分析技术，我们根据EMG的测量差异扰动此模拟。这种方法的价值在于我们能够创建受试者特异性肌肉激活模式的能力，例如在肌肉骨骼疾病（如膝关节骨关节炎）患者中观察到的能力。我们的工作可能会导致重要的新工具进行定量诊断和评估。