Methods to improve the reliability of biomechanical gait kinematic data.

提高生物力学步态运动学数据可靠性的方法。

• 批准号: RGPIN-2014-04079
• 负责人: Ferber, Reed
• 金额: $ 2.84万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=650627
• 关键词: Methods; improve; reliability; biomechanical; gait

Summary of Proposal (3800 characters)** Biomechanical gait analysis is one of the most ubiquitous research methods for analysing sport performance or evaluating pathologic gait. However, a significant limitation with biomechanical gait research is that most laboratories function in isolation and thereby collect data on a small number of subjects. For the past 4 years, we have developed a worldwide network of 30 research and clinic partners all linked to a common research database: FeBE (Fetch By Email). The overarching purpose is to create an open-source 3D biomechanical database and allow researchers access to the data for the purpose of hypothesis-driven research. Thus, it is imperative we ensure the data are reliable and valid across the contributing sites.* The most commonly recognized problem is the day-to-day variability that may be present due to placement of retro-reflective markers over the skin on specific anatomical landmarks. This variability is especially important when the same subject is being tested on more than one occasion or when different people collect data from multiple sites. While the results of our previous research provided increased confidence in the data being added to FeBE, we continued to develop novel methods to screen and improve data reliability.* Considering that a large portion of the data in FeBE had been collected by one individual, with 15 years of experience in clinical anatomy and over 500 gait analyses, this database, in our opinion, constitutes a critical resource that makes an in-depth study of marker placement error feasible. We proceeded to use these reference data (n=400) as a means to develop a standard anatomical model involving a unique integration of two seemingly disparate NSE disciplines: morphometrics and biomechanics. Most importantly, this model provided the tools needed to quantitatively detect marker placement errors. Therefore, this NSERC Discovery Grant proposal will build on our past research and continue to develop novel methods to improve the reliability and validity of kinematic gait data and train future biomechanists and gait analysis experts.* The long-term objective of my research program is to create tools to support interdisciplinary multi-centre biomechanical investigations. The short-term objective of this proposal is to ensure that data are accurate, reliable, and repeatable by developing novel statistical methods for data screening and kinematic variable selection as well as robust training methods and software tools for biomechanists. We propose to focus on four Specific Aims: Aim 1 is focused on developing original and innovative statistical methods to improve kinematic data collection accuracy, reliability, and repeatability. Aims 2, 3, and 4 focus on novel research questions to improve marker placement accuracy along with novel training methods.* Reliability of gait biomechanical data is an important topic within NSE research considering that NSERC strives to "facilitate the pooling of knowledge, resources and expertise" as well as "foster global research platforms and promote the internationalization of research and training." To our knowledge, the development of FeBE and our overarching approach is completely novel and speaks directly to achieving these objectives and priorities. This proposed research will improve the quality of data collected in our lab, as well as gait labs around the world. By ensuring that data are accurate and repeatable, and through the development of novel statistical methods for data screening, we will ensure that biomechanical researchers have access to the best tools. These tools will ultimately improve our progress in using 3D gait biomechanics for research purposes.

**生物力学步态分析是分析运动表现或评估病理步态最普遍的研究方法之一。然而，生物力学步态研究的一个重大限制是，大多数实验室都是孤立的，因此只能收集少数受试者的数据。在过去的4年里，我们已经建立了一个由30个研究和诊所合作伙伴组成的全球网络，所有这些合作伙伴都连接到一个共同的研究数据库：FeBE （Fetch By Email）。总体目的是创建一个开源的3D生物力学数据库，并允许研究人员访问数据，以进行假设驱动的研究。因此，我们必须确保数据在贡献站点之间是可靠和有效的。*最常见的问题是，由于在皮肤上的特定解剖标志上放置了反向反射标记，可能会出现日常变化。当同一主题在多个场合进行测试或当不同的人从多个站点收集数据时，这种可变性尤为重要。虽然我们之前的研究结果增加了对添加到FeBE的数据的信心，但我们继续开发新的方法来筛选和提高数据的可靠性。*考虑到FeBE中的大部分数据是由一个具有15年临床解剖学经验和超过500次步态分析的个体收集的，我们认为该数据库构成了一个关键资源，使标记放置误差的深入研究成为可能。我们继续使用这些参考数据（n=400）作为建立标准解剖模型的手段，该模型涉及两个看似不同的NSE学科：形态计量学和生物力学的独特整合。最重要的是，该模型提供了定量检测标记放置错误所需的工具。因此，这项NSERC发现拨款提案将建立在我们过去的研究基础上，并继续开发新的方法来提高运动学步态数据的可靠性和有效性，并培养未来的生物力学家和步态分析专家。*我的研究计划的长期目标是创造工具来支持跨学科的多中心生物力学研究。本提案的短期目标是通过开发用于数据筛选和运动学变量选择的新颖统计方法以及健壮的训练方法和生物力学家的软件工具，确保数据准确，可靠和可重复。我们建议关注四个具体目标：目标1侧重于开发原始和创新的统计方法，以提高运动学数据收集的准确性，可靠性和可重复性。目标2、3和4侧重于新颖的研究问题，以提高标记放置的准确性以及新颖的训练方法。*步态生物力学数据的可靠性是NSE研究中的一个重要课题，因为NSERC致力于“促进知识、资源和专业知识的汇集”以及“培育全球研究平台，促进研究和培训的国际化”。据我们所知，FeBE的开发和我们的总体方法是完全新颖的，并直接谈到实现这些目标和优先级。这项拟议的研究将提高我们实验室以及世界各地步态实验室收集的数据的质量。通过确保数据的准确性和可重复性，以及通过开发用于数据筛选的新颖统计方法，我们将确保生物力学研究人员能够使用最好的工具。这些工具最终将提高我们在研究中使用3D步态生物力学的进展。