Dynamic Simulations and Data Mining of Single-Leg Jump Landing: Implications for Anterior Cruciate Ligament Injury Prevention
Date of Award
Doctor of Philosophy
Jeffrey A. Reinbolt
William R. Hamel, J. A. M. Boulet, Songning Zhang
It is estimated that 400,000 anterior cruciate ligament (ACL) injuries occur in the United States each year with the cost of ACL reconstruction surgery and rehabilitation exceeding $1 billion annually. The majority of ACL injuries are non-contact injuries occurring during cutting and jump landing movements. Because the majority of the injuries are non-contact injuries there is the potential to develop programs to reduce the risk of injury. Given our understanding of the joint kinematics and kinetics that place an individual at high risk for ACL, researchers have developed neuromuscular training programs that focus on improving muscle function in order to help the muscles support and stabilize the knee during the dynamic movements that increase the strain on the ACL. Yet, despite the implementation of these neuromuscular-based ACL injury training intervention programs ACL rates continue to rise. Thus the objective of this dissertation is to determine the cause and effect relationship between joint biomechanics and muscle function with respect ACL injury.
There are four studies in this dissertation. The first two studies rely heavily on the development of subject-specific musculoskeletal models to analyze muscle contribution during single-leg jump landing. These studies will generate forward dynamic simulations to estimate muscle force production and contribution to movement. The results of these studies will aid in the development of muscle-targeted ACL injury training intervention programs. The last two studies will employ data mining techniques; such as, principal component analysis (PCA) and wavelet analysis along with stability methods from control theory, to evaluate an individual’s risk of ACL injury and determine how muscle function differs for individuals at varying levels of injury risk. The goal will be to use this information to develop a more robust ACL injury prescreening tool.
The use of both dynamic simulations and data mining techniques provides a unique approach to investigating the relationship between joint biomechanics and muscle function with respect to ACL injury. And this approach has the potential to gain much needed insight about the underlying mechanism of ACL injury and help progress ACL research forward.
Morgan, Kristin Denise, "Dynamic Simulations and Data Mining of Single-Leg Jump Landing: Implications for Anterior Cruciate Ligament Injury Prevention. " PhD diss., University of Tennessee, 2014.