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.

提案摘要（3800 个字符）** 生物力学步态分析是分析运动表现或评估病理步态的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运作的，因此只能收集少数受试者的数据。 在过去 4 年里，我们开发了一个由 30 个研究和临床合作伙伴组成的全球网络，所有合作伙伴都链接到一个共同的研究数据库：FeBE（通过电子邮件获取）。首要目的是创建一个开源 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中可靠且有效。*最常见的问题是由于将反光标记放置在特定解剖部位的皮肤上而可能出现的日常变化。地标。 当同一受试者在多个场合进行测试或不同的人从多个站点收集数据时，这种可变性尤其重要。 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可靠性。* 考虑到 FeBE 中的大部分数据是由一个人收集的，我们认为，该数据库拥有 15 年的临床解剖学经验和 500 多项步态分析，构成了一个关键资源，使深入研究标记放置错误成为可能。 我们继续使用这些参考数据 (n=400) 作为开发标准解剖模型的手段，该模型涉及两个看似不同的 NSE 学科的独特整合：形态测量学和生物力学。 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，继续开发新方法来提高运动学步态数据的可靠性和有效性，并培训未来的生物力学学家和步态分析专家。*我的研究计划的长期目标是创建工具来支持跨学科多中心生物力学研究。 该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生物力学稳健的培训方法和软件工具来确保数据准确、可靠和可重复。 我们建议重点关注四个具体目标： 目标 1 专注于开发原创和创新的统计方法，以提高运动数据收集的准确性、可靠性和可重复性。 目标 2、3 和 4 侧重于新颖的研究问题，以提高标记放置准确性以及新颖的训练方法。*考虑到 NSERC 致力于“促进知识、资源和数据的汇集，步态生物力学数据的可靠性是 NSE 研究中的一个重要主题”。专业知识”以及“培育全球研究平台并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，并且直接说明了实现这些目标和优先事项。 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，并通过开发新的数据筛选统计方法，我们将确保生物力学研究人员能够获得最好的工具。 这些工具最终将提高我们在使用 3D 步态生物力学进行研究方面的进展。