SBIR Phase II: 3D Markerless Motion Capture Technology For Gait Analysis

Award ID: 2153138

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project benefitd the 70 million people struggling with gait disorders in the US. Gait analysis monitors how individuals walk to predict or prevent injuries and track rehabilitation progress. Due to the potentially high equipment costs, space requirements, and required technical expertise to collect and interpret results, detailed gait analyses are limited to a few centers and mainly used for surgical decision-making. This project uses Artificial Intelligence (AI) for gait analysis at roughly 70% less than typical costs, as well as offering reduced set-up time and improved workflow efficiency. The proposed project will address the existing technological and commercial barriers that make gait analysis an exclusive clinical tool restricted to only 300 gait labs across the country. This project will develop a cost-effective markerless motion 3D-tracking system. Technical activities include: (1) increase the motion database to a diverse population, including children and patients with mobility impairments; (2) improve the computer vision algorithms to account for multiple individuals within the camera systems field of view; (3) integrate EMG and Force Plates; (4) implement skeletal geometry estimation from computed tomography; (5) develop a self-calibration system that can operate with HD low cost cameras; and (5) improve accuracy and usability substantially to reduce the expertise required for computational gait analysis. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Alastair Monk