Statistical Models for Establishing a Control Data set for Biomechanical Gait Analysis

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This proposal forms a new collaboration between Queen's University (Dr. K.J. Deluzio) and a Canadian company (HAS-Motion, Kingston, ON). The aim is to develop quality assurance criteria for 3D motion capture data of human gait. HAS-Motion's primary product, Visual3D, facilitates the analysis of biomechanical data for one, or a small number of subjects, with the output being an assessment report. HAS-Motion has identified a need to enhance Visual3D with statistical processing to facilitate public data sharing and multicenter studies. The long term goal of this collaboration is to build a public database for data sharing in human gait analysis. Our initial milestone is the development of statistical criteria for assessing data quality and biomechanical/functional classification. The plan is to collect motion data from a group of healthy young subjects as they walk, run, and perform simulated abnormal walking. Then we will develop a statistical pattern recognition tool to classify the gait patterns and predict group membership (normal gait, running, perturbed gait). This outcome of this research will have a direct benefit to the company, and it is also directly related to Dr. Deluzio's goal to detect and treat musculoskeletal problems. The project will advance Canada's position within the field of biomechanics and within science in general as a leader in promoting public access to data. Labs requiring large sets of control data (e.g. clinical and military research labs) often cannot devote their clinical research resources to develop appropriately matched control data sets. In the long term, with large enough datasets for control and impaired conditions, the research can improve the ability to predict and optimize patient-specific clinical outcomes with a high level of confidence.