Identification of physical parameters of biological and mechanical systems under whole-body vibration

Abstract

for their guidance and suggestions. Furthermore, I would like to thank Jonathan DeShaw for the experiments and data processing. I also would like to thank Ulysses Grant for his assistance on the experiments. Without them, I could not have run my experiments or built my data so smoothly.Additionally, I would like to express my special thanks to my parents, Junmei Qiao and Yulan Fu. I can feel love and support from them, even if over more than ten thousand kilometers.Finally, my sincere thanks to all my friends for your appearances in my life and sharing happiness with me.iii ABSTRACTThe identification of the physical parameters (mass, stiffness, and damping) of structural, mechanical, and biomechanical systems is a major challenge in many applications, especially when dealing with old systems and biological systems with heavy damping and where environmental noises are presented. This work presents a novel methodology called eigenvector phase correction (EVPHC) to solve for the physical parameters of structural and biomechanical systems even with the existence of a significant amount of noise. The method was first tested on structural/mechanical systems and showed superior results when compared with an iterative method from the literature.EVPHC was then developed and used to identify the physical parameters of supine humans under vertical whole-body vibration. Modal parameters of fifteen human subjects, in the supine position, were first identified in this work using experimentation under vertical whole-body vibration. EVPHC was then used to solve an inverse modal problem for the identification of the stiffness and damping parameters at the cervical and lumbar areas of supine humans. The results showed that the resulting physical parameters were realistically close to those presented in the literature. The proposed human model was able to predict the time histories of the acceleration at the head, chest, pelvis, and legs very closely to those of the experimental measured values. A scaling methodology is also presented in this work, where an average human model was scaled to an individual subject using the body mass properties. iv PUBLIC ABSTRACTThe purpose of the work presented is to introduce a novel methodology to identify the unknown physical properties of structural, mechanical, and biomedical systems. Data is collected from vibration experiments on human beings. Then the collected data is utilized to characterize the vibration of the supine human and further identify the physical properties of the supine human. v

Effect of gender, stature, and body mass on immobilized supine human response during en route care transport

“…These peaks are similar to what would normally be seen from lab experimentation and mathematical modeling behavior. 19,24,[32][33][34][35][36] Figure 3 shows that the peak frequency of the pelvis of the female subjects is larger than that of the male subjects and was shifted to the left. This is similar to the finding 17 in seated subjects when studying AP.…”

“…Interestingly, the shapes of transmissibility graphs at the pelvis of the supine subjects for females and males are similar to those of the AP for seated subjects; both demonstrate a large peak at 5 Hz and a smaller peak around 10 Hz. It is very interesting to see that human models [32][33][34][35][36] have also predicted two peaks at similar locations. The area under the vertical-acceleration-based transmissibility results shows that female subjects, in general, experienced higher vertical translational movements than male subjects; this could also be attributed to the body mass of the subjects (Section "Biological information"), as the average mass of female subjects was less than that of male subjects.…”

Hybrid artificial genetic – neural network model to predict the transmission of vibration to the head during whole-body vibration training

“…Lumped parameter models and finite element models fall under this approach. The complexity of this approach depends on the number of segments and the proposed degree of freedom [20][21][22][23]. In the second approach, the whole-body is treated as a black box and the goal of the model is to predict the response of some parts of the body given a specific input.…”