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

我的研究生涯一直专注于开发用于分析人体运动的工程工具。与我的学生一起工作，我的研究为人类运动数据的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大量的可变性，可能会被实验方法中的不确定性所混淆。因此，确保纵向研究实验室内、数据比较实验室之间或多中心研究中的数据可靠和一致是一项重大而有价值的挑战。我建议通过量化和消除这种令人讨厌的变化来改善我们的分类技术的鲁棒性和灵敏度。这种新方法的一个主要特点是，它可以应用于已经收集的数据，而不仅仅是规定未来应该如何收集数据。我们已经将我们的分类工具应用于膝关节功能及其因骨关节炎（OA）而恶化的示例机制，这促使我们在计算建模方面开展工作。生物关节，就像它们的机械对应物一样，在过度负荷条件下逐渐恶化。然而，我们不能在没有侵入性和潜在混淆的外科手术的情况下测量内部膝关节载荷或肌肉力，因此，我们依赖于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步态建模增加了一个实质性的挑战，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应对。肌肉力量对关节接触力有很大贡献，并且患有OA的受试者使用他们的肌肉与健康受试者不同。为了反映这一现实，模型必须被告知使用肌电图（EMG）记录的肌肉激活模式。一种方法是使用EMG驱动模型，其中EMG用于驱动肌肉骨骼模型中的肌肉。然而，肌电驱动的模型是有限的，由于，除其他事项外，不令人满意的解决方案的肌电力的关系。我们已经开发了一种替代方法，最好的特点是“EMG知情”优化，其中创建一个特定的模拟，然后我们扰动这个模拟根据测量的差异，在EMG，基于我们的数据分析技术。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，例如在膝关节骨关节炎等肌肉骨骼疾病患者中观察到的模式。我们的工作可能会导致重要的新工具，定量诊断和评估的治疗。**

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. **