Principal Investigator: Thomas P. Andriacchi, PhD Project Category: Bone & Joint - 2004 Description: This is a project to design, develop and evaluate novel systems using multiple video sensors that will efficiently and accurately model and measure human movements using a markerless technique. The principal goal of the proposed five-year effort is to design, develop and evaluate novel systems using multiple video sensors that will efficiently and accurately model and measure human movements as well as structure. Such systems will provide 3-D measurements of the movements and structures of whole human bodies, body parts, and joints. The availability of these descriptions will allow us to develop fundamental methodologies and dramatically improved methods for diagnosis and treatment of movement-related disorders, animation studies and human gait analysis. The University of Maryland will serve as the lead institution, with Stanford University and New York University participating as core partners. We propose an integrated research program supported by strong outreach efforts in education and an effective management plan. The findings of our research have the potential to produce a revolutionary change in our ability to measure and subsequently utilize models of human movement and structure. For example, by combining marker less movement patterns and other measurements extracted from body parts and whole bodies, we can develop new ways of measuring human movements associated with pathological functions. For example, our research on the fusion of video-based markerless motion capture techniques with high-resolution MRI models of subject- specific anatomy and dynamic simulations of movement will lead to the development and validation of substantially improved models of musculoskeletal function. Here, the motions of the limb segments during complex activities will be related to the motions of the underlying skeletal structures. Expected Outcome: Ultimately it should be possible to obtain in vivo measures of such quantities as ligament strain, muscle elongation, and joint contact movement during unencumbered motion. Markerless motion estimation and analysis will lead to much more accurate diagnosis and treatment of movement-related disorders with zero setup time, decreased operator variability, potential for more standardized diagnosis and earlier detection of disorders and development of normative databases for enhanced diagnostic/treatment efficacy. Funding Source: National Science Foundation Funding Status: Active |
