Asymmetric Walking Protocol for Optimal Post-ACL Reconstruction Rehabilitation

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Project Abstract The primary goal of the proposed work is to provide the PI with advanced biomedical research training, outstanding mentorship, and protected time to become a leading independent researcher in post-anterior cruciate ligament reconstruction (ACLR) rehabilitation. A significant consequence of the approximately 250,000 anterior cruciate ligament injuries that occur annually in the United States is that unresolved neuromuscular impairments often lead to the development of detrimental knee osteoarthritis and other debilitating comorbidities. Despite extensive rehabilitation, protracted deficits in gait mechanics remain and directly contribute to detrimental knee loading. Yet, a promising finding from stroke research is that an asymmetric walking protocol can disrupt maladaptive gait mechanics and lead to the adoption of new, healthy gait patterns. While the success of the asymmetric protocol in correcting adverse gait patterns is often assessed by the magnitude of the between-limb gait speed perturbation, this novel intervention to our knowledge has never been employed in post-ACLR patients. Thus, I will employ experimental gait analysis, computational modeling, biosignal processing, and machine learning to restore healthy post-ACLR gait mechanics and reduce knee loading as outlined by the following aims: Aim 1. Evaluate the effectiveness of asymmetric walking protocol gait perturbation magnitudes in restoring healthy gait in post-ACLR individuals. Aim 2. Develop patient-specific models to evaluate the impact asymmetric walking protocol gait perturbation magnitude has on reducing detrimental knee loading in post-ACLR individuals. Aim 3. Generate personalized data-driven clinical algorithm to rapidly and non-invasively predict knee loads in a clinical setting. The results of this research will yield new therapeutic interventions and treatment guidance to improve post- ACLR rehabilitation outcomes. The successful execution of the proposed work will involve a strong team of interdisciplinary researchers with skills in signal processing, machine learning, medicine, biomedical engineering, computational modelling, and physical therapy. The PI has assembled a dynamic team with a superb reputation for mentoring others and they will provide her with research guidance in addition to career and professional development direction and support. This mentorship combined with strong institutional support, state-of-the-art resources, and facilities, and dedicated protected time will allow her to successfully perform the research and training activities outlined in her K01 Mentored Research Scientist Development Award.