It is widely accepted that the mechanical environment within the knee, or more specifically, increased or altered stresses or strains generated within the cartilage, is a leading cause of knee osteoarthritis (OA). However, a significant unfulfilled technological challenge in musculoskeletal biomechanics and OA research has been determining the dynamic mechanical environment of the cartilage (and other components) resulting from routine and non-routine physical movements. There are two methods of investigating musculoskeletal joint mechanics that have been used to date: 1) forward and inverse multibody dynamic simulations of human movement and 2) detailed quasi-static finite element modeling of individual joints. The overwhelming majority of work has been focused on musculoskeletal multibody dynamics modeling. This method, in combination with experimental motion capture and analysis, has been integral to understanding torques, muscle and ligament forces, and reaction forces occurring at the joint during activities such as walking, running, squatting, and jumping as well as providing key insights into musculoskeletal motor control schemes. However, multibody dynamics simulations do not allow for the detailed continuum level analysis of the mechanical environment of the cartilage and other knee joint structures (meniscus, ligaments, and underlying bone) within the knee during physical activities. This is a critical technology gap that is required to understand the relationship between functional or injurious loading of the knee and cartilage degradation. We have developed a detailed neuromuscularly activated dynamic finite element model of the human lower body and have used this model to simultaneously determine the dynamic muscle forces, joint kinematics, contact forces, and detailed (e.g., continuum) stresses and strains within the knee (cartilage, meniscus, ligaments, and bone) during several increasingly complex neuromuscularly controlled and actuated lower limb movements. Motion at each joint is controlled explicitly via deformable cartilage-to-cartilage surface contact at each articular surface (rather than idealized as simple revolute or ball and socket joints). The major muscles activating the lower limb are explicitly modeled with Hill-type active force generating springs using anatomical muscle insertion points and geometric wrapping. Muscle activation dynamics were determined via a constrained optimization scheme to minimize muscle activation energy. Time histories of the mechanical environment of all soft tissues within the knee are determined for a simulated leg extension.
Developing a control framework for self-adjusting prosthetic sockets incorporating tissue injury risk estimation and Generalized Predictive Control
