Empirical Quantification and Computational Modeling of Contributions to Knee Joint Exposures

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Deep knee bending (high flexion) postures, such as squatting and kneeling, are part of many common activities of daily living, including household chores, playing with children, and religiously and culturally significant practices such as prayer. For trade workers (carpet layers, manual materials handlers, miners), the high flexion needed to accomplish floor-level tasks has been associated with increased risk of knee injury and tissue damage, costing the industry and the individual in lost time, productivity, and quality of life. The proposed research program will quantify factors that are known to contribute to knee joint exposures for the full range of flexion and then use these findings to refine a biomechanical model of the human knee joint that is applicable to all knee flexion angles.Accurate biomechanical modeling is essential to a good understanding of the function and malfunction of the human knee joint, but we are lacking the building blocks required to create a physiologically accurate knee joint model for high flexion tasks. The proposed research will systematically quantify parameters that define how the components of the knee joint act and interact to create knee joint motion during high flexion activities of daily living. In addition, the contact between the back of the thigh and calf that is unique to high flexion tasks will be studied to determine its effect in off-loading the knee joint and its relationship with body size. These findings will facilitate the prediction of knee joint exposures that may lead to injury for high flexion activities of daily living. By providing this detailed understanding of what the knee joint is exposed to in high flexion, my research will contribute to applied discoveries in work task and tool modification, biomaterials and prosthetics design, and injury prevention and treatment that will reduce the personal and econonical burdens associated with knee joint injury and disease.