Master of Science, Miami University, 2020, Mechanical and Manufacturing Engineering

Vibration based monitoring techniques are widely used to detect damage, monitor the growth of inherent defects, system identification, and material parameter estimation for various engineering applications. These techniques present a non-invasive and relatively inexpensive tool for various biomedical applications, for example, in characterizing the mechanical properties of the bone and muscles of humans as well as animals. In recent years, it has been shown that fundamental natural frequencies and corresponding damping ratios can be correlated to the bone health quality indicators as associated with osteoporosis, osteoarthritis etc. In this research, through the investigation of clinical data, an analysis procedure is developed to investigate the correlation between the damping properties associated with both lower and higher modes of vibration and bone health quality. Subsequently, a data-driven system identification tool for reconstructing the parameters (mass, stiffness, damping distributions) in a low-dimensional human model is developed which utilizes selected measurements from the clinical study. It is anticipated that the analysis process and parameter identification techniques presented here can be developed and tuned for any individual human model and can be can be used as osteological monitoring tool for predicting early diagnostics pre-cursors of the bone or muscle related conditions or diseases.

Committee: Kumar Singh (Advisor); James Chagdes (Committee Member); Mark Walsh (Committee Member) Subjects: Biomechanics; Biomedical Engineering; Mechanical Engineering; Osteopathic Medicine

MS, University of Cincinnati, 2021, Medicine: Biostatistics (Environmental Health)

Bone damping is a non-invasive measure of bone fragility and is identified as a better predictor of osteoporosis (OP) related bone fracture/fragility than bone mineral density (BMD). Subject with higher damping value demonstrates a heightened resistance to fracture. The purpose of this study was to identify the predictors of bone shock absorption (BSA) capacity measured as a damping factor by using model selection and multivariate multiple regression (MMR) method as well as regression tree. The main dataset was from an existing Cincinnati Lead Study (CLS) cohort. It is a prospective and longitudinal study examined early and late effects of childhood lead exposure on growth and development. The results of this study indicated that cortical vBMD, cortical thickness, endosteal circumference, cortical section modulus, current weight, and the number of pregnancies carried until the 3rd trimester were significant predictors of bone damping factor based on the method of model selection and MMR. Among the predictors with top nine highest variable importance values in regression tree method, four are the same as significant predictors from MMR analysis. Those are current weight, cortical section modulus, cortical vBMD, and endosteal circumference. Cortical section modulus and cortical vBMD have positive relationship with damping factor; however weight and endosteal circumference have negative relationship with damping factor. All variables' relationships with damping factor are clinically significant. Lack of dataset from normal people to compare the differences and the missingness of the data are the limitation of the study. Current weight, cortical section modulus, cortical vBMD, and endosteal circumference are significant predictor of damping factor based on the study results. They are biologically relevant to damping and statistically significant in the damping model.

Committee: Amit Bhattacharya (Committee Member); Marepalli Rao Ph.D. (Committee Chair) Subjects: Biostatistics