PROJECT SUMMARY/ABSTRACT It is widely accepted that failure to restore pre-injury biomechanics after anterior cruciate ligament reconstruction (ACL-R) surgery is one of the key contributing factors to the high prevalence of post-traumatic osteoarthritis (PTOA). Precision rehabilitation, which refers to the delivery of the right feedback to the right patient at the right time, is now a feasible approach for PTOA prevention given recent advances in wearable sensing and computer vision technologies. Flexible and unobtrusive skin patches can objectively quantify movement out of the clinic and deliver real-time haptic feedback, while simple videos from smartphones can assess physical therapy quality and deliver corrective visual or auditory feedback. To effectively apply emerging smart-health technologies toward PTOA prevention, the multi-modal and multivariate data produced by these sensors must be distilled to identify digital biomarkers of PTOA that can be targeted with biofeedback therapy in the future. Accordingly, the central objective of this proposed work is to determine if characteristics of gait extracted from video and wearable sensors (digital biomarkers) can predict longitudinal changes in cartilage microstructure (early PTOA) extracted from quantitative Magnetic Resonance Imaging (qMRI). Our central hypothesis is that future risk of PTOA can be predicted in the first few months after surgery using passively collected data from wearable sensors and video. This hypothesis is supported by our previous work on pre-arthritic subjects, where we demonstrated that wearable sensing data could predict detrimental changes in cartilage microstructure that are indicative of OA risk. To accomplish the overall objective of this work, physical therapy, natural environment ambulation, and cartilage health will be monitored longitudinally. Exercise correctness during pre- and post-operative physical therapy will be quantified using computer vision and machine learning algorithms. Out-of-lab movement will be monitored at baseline (3 weeks), 3, and 9 months after surgery with epidermal sensors placed on the thighs and shanks. Quantitative MRI data will be collected at baseline (3 weeks), 3 and 18 months after the surgery. Specifically, we will determine (1) if gait symmetry restoration measured by wearable sensors can predict qMRI changes up to 18 months post-surgery and (2) if physical therapy quality, to the extent that is quantifiable with passive computer vision algorithms, can predict gait symmetry restoration up to 9 months post-surgery. This work is innovative because it breaks with the current norms of studying the role of biomechanics in PTOA in the laboratory. Instead, we will use wearable sensing, computer vision, and machine learning to generate previously unavailable knowledge on the role of natural environment biomechanics. If successful, this work could enable personalized, technology- assisted rehabilitation—a paradigm shift in clinical care. Additionally, the discovery of new PTOA biomarkers could improve the efficiency of clinical trials for new surgical techniques, while the proposed framework is also extensible to the study and prevention of primary OA, and possibly other orthopaedic conditions.

PROJECT NARRATIVE Every year, nearly 400,000 Americans rupture their anterior cruciate ligament and more than half of them develop post-traumatic osteoarthritis (PTOA) within 15 years. By objectively capturing movement during both natural environment ambulation and physical therapy, the proposed research will inform the development of more effective rehabilitation therapies assisted by wearable technology and computer vision/machine learning, promising to halt PTOA and extend the lifespan of natural joints.