This Faculty Early Career Development Program (CAREER) project will investigate new non-invasive methods to automate the identification of individuals at elevated risk for Anterior Crucial Ligament (ACL) injuries. Though directed care by trained physical therapists has been shown to effectively prevent such injuries, the resources available for such re-training are limited. Unfortunately, the diagnosis of those at greatest risk for such ligament tears depends upon expensive, time-consuming observations by trained clinicians using qualitative metrics that have been shown to be challenging to consistently apply. This project will develop automated prehabilitative techniques to diagnose those at increased risk for ACL injury by constructing and quantitatively analyzing an individual-specific musculoskeletal model of motion. This will ensure the reliable behavior of the diagnostic technique created in this project. Importantly, this broadly deployable approach will add a preventive component for the treatment of ACL injuries that afflict more than 200,000 people annually and drastically affect a patient's quality of life thereafter. More broadly, these models of motion will fundamentally change how human assistive devices are controlled. The ability to leverage systems-based techniques during control will enable the wide distribution of such assistive devices, which currently require careful physician and engineer-guided tuning. This project promotes the progress of science and contributes significantly to advance the national health. The integrated education plan not only has direct impact on undergraduate education but also provides great opportunities for K-12 students to explore STEM careers by exposing them to robotic control via hands-on examples.
This project supports the development of numerical techniques to diagnose an increased risk for ACL injury given articulated pose observations from a set of cameras of an individual performing a functional motion screen. To construct and verify the reliable behavior of these methods, this project will explore the following three research objectives: First, a new convex optimization tool to identify all parameterizations and associated control inputs of a hybrid dynamical model that explain a given set of observations. Second, a novel diagnostic tool that tractably evaluates injury risk metrics across all identified model parameterizations to ensure that misdiagnosis does not occur. Finally, a real-world, two yearlong longitudinal evaluation on 25 healthy subjects and 125 patients who are undergoing rehabilitation after an ACL reconstructive surgery. These innovations in dynamics, controls, and optimization will enable the development of a completely automated, robust, individual, and motion specific technique to identify those at increased risk for ACL injuries that cannot be currently achieved with the existing human-guided approach.
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.
