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    Predicting ground reaction forces during normal gait by solving tracking optimization problem
    (Türk-Alman Üniversitesi Fen Bilimler Enstitüsü, 2024) Atmaca, Kerim
    Ground Reaction Force (GRF) is a central biomechanical parameter in gaitanalysis, critical for understanding human locomotion mechanics. Traditional methods for GRF data acquisition, though precise, are often constrained by their resource-intensive nature. In response to these limitations, recent research has explored alternative approaches using musculoskeletal models and machine learning algorithms for GRF prediction. This thesis contributes to these efforts by presenting an approach that employs a tracking optimization problem frame work to predict GRF in human gait analysis. This study, grounded in biome chanics, leverages computational tools to analyze gait data from 10 healthy individuals aged between 14 and 54. Opensim, a state-of-the-art biomechanics simulation software, was utilized for modeling and simulation tasks. This pro cess involved biomechanically scaling individual-specific musculoskeletal models based on static pose marker data and employing inverse kinematics for determin ing joint angle trajectories. These kinematic trajectories were then inputted into a tracking optimization algorithm, designed to predict GRF. The biomechanical effectiveness of this approach was validated through its strong correlation with normative kinetic data, achieving an average Pearson Correlation Coefficient (PCC) of 0.88 and a Normalized Root Mean Square Error (NRMSE) of 0.12 . The results demonstrate the effectiveness of this integrated method in biome chanical gait analysis and suggest potential applicability in clinical settings for patient rehabilitation and care. However, to fully ascertain this method’s biome chanical accuracy and clinical utility, further validation in a broader population is necessary. This study represents an effort to blend biomechanical knowledge with advanced computational techniques, aiming to improve the practicality and accessibility of gait analysis in healthcare and research environments.