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，我的研究对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混淆了大量的可变性。因此，在纵向研究的实验室内、在数据比较的实验室之间或在多中心研究中确保可靠和一致的数据是一项重大而有价值的挑战。我建议通过量化和消除这种讨厌的变化来提高我们分类技术的鲁棒性和灵敏度。这种新方法的一个关键特点是，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）引起的恶化作为范例机制，这激发了我们在计算建模方面的工作。生物关节和机械关节一样，在过度负荷条件下会逐渐恶化。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。******建模OA步态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满足。肌肉力对关节接触力的贡献很大，骨性关节炎患者使用肌肉的方式与健康人不同。为了反映这一现实，模型必须使用肌电图（EMG）记录的肌肉激活模式。一种方法是用肌电图驱动模型，肌电图被用来驱动肌肉骨骼模型中的肌肉。然而，肌电驱动模型的局限性在于，肌电-力关系的解并不理想。我们已经开发了一种替代方法，最好的特点是“肌电信息”优化，其中创建了一个特定于受试者的模拟，然后我们根据肌电图的测量差异，基于我们的数据分析技术来干扰这个模拟。这种方法的价值在于我们能够创造出特定于受试者的肌肉激活模式，就像在膝关节骨关节炎等肌肉骨骼疾病患者中观察到的那样。我们的工作可能会为定量诊断和治疗评估带来重要的新工具。**

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. **