The way forces are transmitted through the musculoskeletal system plays a critical role in joint health. When these forces are abnormally high, long lasting, or applied too frequently, they can contribute to joint degeneration and tissue damage. Accurately capturing these dynamics is essential for understanding musculoskeletal conditions like osteoarthritis.

Traditionally, joint-level kinetics during movement are modeled using inverse dynamics—an approach based on Newtonian mechanics. This method combines body segment motion data with external forces (e.g., ground reaction forces) to estimate net joint forces and moments. However, it does not account for how individual muscles contribute to joint loading, often leading to underestimates of the actual forces experienced by joint tissues.

To bridge this gap, our research integrates neuromuscular activation data into musculoskeletal models. By incorporating electromyography (EMG), biomechanical modeling, and optimization techniques, we estimate the role of muscle forces in shaping joint contact forces during movement. We also use wearable accelerometry to quantify in vivo loading frequency, adding another layer of insight into joint mechanics.

Together, these methods provide a more complete picture of how joint loading patterns influence disease progression and recovery, particularly in the context of osteoarthritis and orthopaedic interventions.