I-Corps: Embedded Machine Vision for Accurate Gait Analyses and Body Movement Measurements

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The broader impact/commercial potential of this I-Corps project is to revolutionize conventional rehabilitation assistant and body movement assessment tools with low-cost machine vision based solutions by maximizing the capability of ubiquitous tablets and 2D cameras, which have significant cost and accessibility advantage over more expensive 3D cameras. A set of tablet/smartphone Apps can be developed base on the embedded machine vision technology, and the team plan to create related services using a subscription model. This innovation will benefit broad healthcare market, which includes rehab facilities, surgery/neurology outpatient clinics, physical therapy clinics, stroke centers, and individuals. Overall, the innovation will make embedded machine vision technology accessible to and serve many population groups in the society. This I-Corps project is based on preliminary results that we obtained on a PC platform to identify and track human body joint points in close to real-time. The prototype leverages novel machine learning algorithms and high performance processors (graphics processing units). Our next phase of research is to generate depth map from a 2D camera, recognize and track body joint points, and score body movements, all on a tablet platform. The challenges include depth generation with 2D images and efficient models and algorithms to execute on a resource constraint embedded platform. The team will leverage embedded deep learning techniques to optimize the algorithms towards an embedded processor architecture on modern tablets so that the proof-of-concept can deliver satisfactory performance. The team also plan to work with a medical doctor and a rehabilitation facility advisor to review and refine the software features and validate the early prototype.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.