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Show COLLEGE OF ENGINEERING UNDERGRADUATE RESEARCH ABSTRACTS 3D MOTION CAPTURE DATA PROCESSING Chris Landes (Nate Godfrey, Andrew Merryweather, Don Bloswick) Department of Mechanical Engineering University of Utah Three-dimensional motion capture and biomechanical modeling has become an important part of digital animation, athletic performance, and the prevention and understanding of musculoskeletal disorders and injuries from complex movements. To make use of the 3 D motion data captured it must first be processed. This process includes multiple steps: (1) raw 2 D marker data acquired during motion capture must be assessed for abnormalities (2) raw 2D data from multiple cameras are combined using a calibration algorithm in order to generate 3 D marker trajectories (3) 3 D marker trajectories are labeled (4) gaps in 3D marker trajectories are filled using splines and pattern tracking algorithms, and (5) a biomechanical model is applied to the 3D marker trajectories for analysis. In this study, 3D motion capture was performed while participants stood up from a chair walked to a hospital bed, laid down, turned from one side to another, sat up in the bed and returned to a chair. This was performed from three different bed heights based of subjects o w n lower leg length with three different side rail configurations (Stryker side rail, Hill R o m side rail, no side rail) for a total of nine different trial conditions. A custom marker set was placed at key anatomical landmarks to define a skeleton model for each participant. An array of 18-V100:R2 cameras (NaturalPoint Inc., Corvallis, OR) tracked the markers. Using the software application AMASS (C-Motion Inc. Germantown, M D ) the 2 D data from all the cameras were processed using the calibration algorithm to generate 3 D marker trajectories. Each marker was then identified and labeled using a custom marker set designed for this study. These labeled 3D marker trajectories were exported to the software application Nexus (Vicon Motion Systems Ltd. UK) for gap filling to increase model consistency. Finally, a scaled biomechanical model was generated for each participant. This model provides researchers with kinematic and kinetic data including, measures of postural stability and balance, measures of slip potential and a host of other important biomechanics variables. Robust initial data processing provides researchers with models to dynamically analyze complex movement and study additional hypotheses without having to initiate further motion capture. These models can be used to evaluate new injury prevention metrics and to create simulations to help answer questions about musculoskeletal disorders and other injuries. |