This thesis presents an engineering approach to the modeling and analysis of custom foot orthotics. Although orthotics are widely used and accepted as devices for the prevention of and recovery from injuries, the design process continues to be based on empirical means. There have been many clinical studies investigating the various effects that the orthotics can have on the kinematics and kinetics of human locomotion. The results from these studies are not always consistent, primarily due to subject variability and experimental nature of the design. Alternatively, a better understanding of the therapeutic effects of custom foot orthotics, as well as designing for optimal performance, can be achieved through simulation-based engineering modeling and analysis studies. Such an approach will pave the way to clarify some of the ambiguous findings found in the clinical studies-based literature. Towards this goal, this research presents a methodical process for the replication of the orthotics’ complex three-dimensional geometry and for the construction of finite element analysis models using estimated nonlinear material properties. As part of this research, laser scanning techniques are used to capture the objects’ details and geometry through generation of point cloud surface images by taking multiple scans from all angles. Material testing and Mooney-Rivlin equations were used to construct the hyperelastic nonlinear material properties. Using the mid-stance phase of gait for loading conditions, the ANSYS finite element package was utilized to run analyses on three different load classifications and the corresponding maximum stresses and deflection results were generated. The results indicate that the simulated models can augment and validate the use of empirical tables for designing custom foot orthotics. They can also provide the basis for the optimal design thicknesses of custom foot orthotics based on an end-users’ weight and activities. From a practical perspective, they can also be useful in further exploring different orthotics, loading conditions, material properties, as well as the effectiveness of orthotics for different foot and lower extremity deformities.

This research details a procedure for the systematic design of custom foot orthotics based on simulation models and their validation through experimental and clinical studies. These models may ultimately be able to replace the use of empirical tables for designing custom foot orthotics and enable optimal design thicknesses based on the body weight and activities of end-users. Similarly, they may facilitate effortless simulation of various orthotic and loading conditions, changes in material properties, and foot deformities by simply altering model parameters. Finally, these models and the corresponding results may also form the basis for subsequent design of a new generation of custom foot orthotics. Two studies were carried out, the first involving a methodical approach to development of engineering analysis models using the FEA technique. Subsequently, for model verification and validation purposes, detailed investigations were executed through experimental and clinical studies. The results were within 15% difference for the experimental studies and 26% for the clinical studies, and most of the probability values were greater than α= 0.05 accepting our null hypothesis that the FEA model data versus clinical trial data are not significantly different. The accuracy of the FEA model was further enhanced when the uniform loading condition was replaced with a more realistic pressure distribution of 70% of the weight in the heel and the rest in the front portion of the orthotic. This alteration brought the values down to within 22% difference of the clinical studies, with the P-values once again showed no significant difference between the modified FEA model and the clinical studies for most of the scenarios. The second study dealt with the development of surrogate models from FEA results, which can then be used in lieu of the computationally intensive FEA-based analysis models in the engineering design of CFO. Four techniques were studied, including the second-order polynomial response surface, Kriging, non-parametric regression and neural networking. All four techniques were found to be computationally efficient with an average of over 200% savings in time, and the Kriging technique was found to be the most accurate with an average % difference of below 0.30 for each of the loading conditions (light, medium and heavy). The two studies clearly indicate that engineering modeling, analysis and design using FEA techniques coupled with surrogate modeling methods offer a consistent, accurate and reliable alternative to empirical clinical studies. This powerful alternative simulation-based design framework can be a viable and valuable tool in the custom design of orthotics based on an individual's unique needs and foot characteristics. With these capabilities, the CFO prescriber would be able to design and develop the best-fit CFO with the optimal design characteristics for each individual customer without relying upon extensive and expensive trial and error ad hoc approaches. Such a model could also facilitate the inspection of robustness of resulting designs, as well as enable visual inspection of the impact of even small changes on the overall performance of the CFO. By adding the results from these studies to the CFO community, the prescription process may become more efficient and therefore more affordable and accessible to all populations and groups.