Methods to improve the reliability of biomechanical gait kinematic data.

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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.