(Montana State University - Bozeman, College of Education, Health & Human Development, 2007) O'Keefe, Kathryn Bernadine; Chairperson, Graduate Committee: Michael E. Hahn.
Biomechanical gait analysis may be used to determine treatment options, evaluate the success of rehabilitation programs or post-surgery recuperation, and provide insight for surgical planning, including functional outcomes for patients. However, gait analysis requires expensive equipment - a limiting factor for many clinical settings. One alternative that has been examined is the utilization of an artificial neural network (ANN) to model nonlinear relationships of gait. Researchers have shown initial success in ANN predictions of pathological conditions in gait as well as modeling other parameters. The purpose of this study was to evaluate the performance of a previously developed three layer feed-forward ANN model at estimating ankle, knee and hip joint moments for subjects with osteoarthrits (OA) from surface electromyography (EMG) signals. The broader purpose was to further validate the use of the ANN model as an alternative, less expensive method to traditional gait analysis. Eighteen subjects (13 female, 5 male) with physician diagnosed OA participated in this study. Each subject completed a full gait analysis session.
