Pipeline Development to Assess Knee Joint Contact Forces as a Risk Factor for Osteoarthritis in Post-Stroke Patients

After a stroke, patients often experience hemiparesis, a condition that causes weakness on one side of the body. Hemiparetic gait can cause abnormal gait patterns that can lead to the deterioration of knee cartilage, resulting in pain, osteoarthritis, and a loss of function. Currently, we do notunderstand the impact of neuromuscular impairment in people post-stroke on knee joint contact forces. Neuromuscular impairments post-stroke may increase knee contact forces, which indicate a greater risk for developing osteoarthritis. Therefore, we aimed to develop a pipeline to calculate knee joint contact forces to assess the risk factors for developing knee osteoarthritis for people post-stroke. We performed musculoskeletal simulations using the software platform OpenSim. Musculoskeletal modeling allows us to estimate joint contact forces, which are too invasive to measure experimentally. Previously collected experimental data included marker motion data, which we used to scale the model and calculate joint angles using Inverse Kinematics. These kinematics, along with ground reaction forces, and the scaled model are inputs into OpenSim MOCO, an algorithm that solves for muscle forces. Through Python scripts, we improved the processing pipelines for these data, enabling us to be ready to batch process the data and estimate the knee joint contact forces.