With the increasing longevity that accompanies advances in medical technology comes a host of other age-related disabilities. Among these are neuro-degenerative diseases such as Alzheimer's disease, Parkinson's disease, and stroke, which significantly reduce the motor and cognitive ability of affected individuals. As these diseases become more prevalent, there is a need for further research and innovation in the field of motor rehabilitation therapy to accommodate these individuals in a cost-effective manner. In recent years, the implementation of social agents has been proposed to alleviate the burden on in-home human caregivers. Socially assistive robotics (SAR) is a new subfield of research derived from human-robot interaction that aims to provide hands-off interventions for patients with an emphasis on social rather than physical interaction. As these SAR systems are very new within the medical field, there is no standardized approach to developing such systems for different populations and therapeutic outcomes. The primary aim of this project is to provide a standardized method for developing such systems by introducing a modular human-robot interaction software framework upon which future implementations can be built. The framework is modular in nature, allowing for a variety of hardware and software additions and modifications, and is designed to provide a task-oriented training structure with augmented feedback given to the user in a closed-loop format. The framework utilizes the ROS (Robot Operating System) middleware suite which supports multiple hardware interfaces and runs primarily on Linux operating systems. These design requirements are validated through testing and analysis of two unique implementations of the framework: a keyboard input reaction task and a reaching-to-grasp task. These implementations serve as example use cases for the framework and provide a template for future designs. This framework will provide a means to streamline the development of future SAR systems for research and rehabilitation therapy.

When conducting motion analysis using 3-dimensional motion capture technology, errors in marker placement on the knee results in a widely observed phenomenon known as “crosstalk” [1-18] in calculated knee joint angles (i.e., flexion-extension (FE), adduction-abduction (AA), internal-external rotation (IE)). Principal Component Analysis (PCA) has recently been proposed as a post hoc method to reduce crosstalk errors and operates by minimizing the correlation between the knee angles [1, 2]. However, recent studies that have used PCA have neither considered exercises, such as cycling (C) and elliptical training (E), other than gait (G) nor estimated the corrected knee axes following PCA correction. The hypothesis of this study is that PCA can correct for crosstalk in G, C, and E exercises but that subject-specific PCA corrected axes differ for these exercises. Motion analysis of the selected exercises were conducted on 8 normal weight (body mass index (BMI) = 21.70 +/- 3.20) and 7 overweight participants (BMI = 27.45 +/- 2.45). An enhanced Helen Hayes marker set with 27 markers was used to track kinematics. Knee joint FE, AA, and IE angles were obtained with Cortex (Motion Analysis, Santa Rosa, CA) software and corrected using PCA to obtain corrected angles for each exercise. Exercise-specific corrected knee joint axes were determined by finding axes that reproduced the shank and ankle body vectors taken from Cortex when used with the PCA corrected angles. Then, PCA corrected gait axes were used as a common set of axes for all exercises to find corresponding knee angles. Paired t-tests assessed if FE-AA angle correlations changed with PCA. Multivariate Paired Hotelling’s T-Square tests assessed if the PCA corrected knee joint axes were similar between exercises. ANOVA was used to assess if Cortex angles, PCA corrected angles, and knee angles using PCA corrected gait axes were different. Reduced FE-AA angle correlations existed for G (p<0.001 for Cortex and p=0.85 for PCA corrected), C (p=0.01 for Cortex and p=0.77 for PCA corrected), and E (p<0.001 for Cortex and p=0.77 for PCA corrected). Differences in the PCA corrected knee axes were found between G and C (p<0.0014). Then, differences were found between Cortex, PCA corrected, and C and E knee angles using the PCA corrected G axes (p<0.0056). The results of this study suggest that if PCA is used to reduce crosstalk errors in motions other than G then it is recommended to adopt the use of a PCA corrected axes set determined from G to produce the PCA corrected angles.

Recently, a constituent based cartilage growth finite element model (CGFEM) was developed in order to predict articular cartilage (AC) biomechanical properties before and after growth. Previous research has noted limitations in the CGFEM such as model convergence with growth periods greater than 12 days. The main aims of this work were to address these limitations through (1) implementation of an exact material Jacobian matrix definition using the Jaumann-Kirchhoff (J-K) method and (2) quantification of elastic material parameters based upon research findings of the Cal Poly Cartilage Biomechanics Group (CPGBG). The J-K method was successfully implemented into the CGFEM and exceeded the maximum convergence strains for both the “pushed forward, then differentiated” (PFD) and “differentiated, then pushed forward” (DPF) methods, while maintaining correct material stress responses. Elastic parameters were optimized for confined compression (CC), unconfined compression (UCC), and uniaxial tension (UT) protocols. This work increases the robustness of the CGFEM through the J-K method, as well as defines an accurate starting point for AC growth based on the optimized material parameters.

Articular cartilage degeneration, called osteoarthritis, in the hip joint is a serious condition that affects millions of individuals yearly, with limited clinical solutions available to prevent or slow progression of damage. Additionally, the effects of high-risk factors (e.g. obesity, soft and hard tissue injuries, abnormal joint alignment, amputations) on the progression of osteoarthritis are not fully understood. Therefore, the objective of this thesis is to generate a finite element model for predicting osteochondral tissue stress and strain in the human hip joint during gait, with a future goal of using this model in clinically relevant studies aimed at prevention, treatment, and rehabilitation of OC injuries. A subject specific finite element model (FEM) was developed from computerized tomography images, using rigid bones and linear elastic isotropic material properties for cartilage as a first step in model development. Peak contact pressures of 8.0 to 10.6 MPa and contact areas of 576 to 1010 mm2 were predicted by this FEM during the stance phase of gait. This model was validated with in vitro measurements and found to be in good agreement with experimentally measured contact pressures, and fair agreement with measured contact areas.

To alleviate the poor ergonomics which surgeons suffer during knee arthroscopy, a semi-robotic device with braking mechanism is created for intraoperative assistance. A slitted ball joint assembly is developed to transmit the clamping force to the arthroscope inside. Ball deformation and stress at various angles to the vertical and clamping forces is recorded through Abaqus Finite Element Analysis (FEA). Contact forces between the scope and inner surfaces of the ball is also computed in FEA at different clamping forces. The von Mises stress occurring in the ball joint is under the yield stress limit for polyethylene, and there is noticeable force preventing the scope from sliding along the ball through-hole under clamping. A prototype of this device is constructed for proof-of-concept.

The tumor microenvironment plays an important role in regulating cancer cell behavior. The tumor microenvironment describes the cancer cells, and the surrounding endothelial cells, fibroblasts, and mesenchymal stem cells, along with the extracellular matrix (ECM). The tumor microenvironment stiffens as cancer undergoes malignant progression, providing biophysical cues that promote invasive, metastatic cellular behaviors. This project investigated the influence of three dimensional (3D) chitosan-alginate (CA) scaffold stiffness on the morphology, growth, and migration of green fluorescent protein (GFP) – transfected MDA-MB-231 (231-GFP) breast cancer (BCa) cells. The CA scaffolds were produced by the freeze casting method at three concentrations, 2 wt%, 4 wt%, and 6 wt% to provide different stiffness culture substrates. The CA scaffold material properties were characterized using scanning electron microscopy imaging for pore structure and compression testing for Young's Modulus. The BCa cell cultures were characterized at day 1, 3, and 7 timepoints using Alamar Blue assay for cell number, fluorescence imaging for cell morphology, and single-cell tracking for cell migration. Pore size calculations using SEM imaging yielded pore sizes of 253.29 ± 52.45 µm, 209.55 ± 21.46 µm, and 216.83 ± 32.63 µm for 2 wt%, 4 wt%, and 6 wt%, respectively. Compression testing of the CA scaffolds yielded Young's Modulus values of 0.064 ± 0.008 kPa, 2.365 ± 0.32 kPa and 3.30 ± 0.415 kPa for 2 wt%, 4 wt%, and 6 wt% CA scaffolds, respectively. The results showed no significant difference in cell number among the 3D CA scaffold groups. However, the 231-GFP cells cultured in 2 wt% CA scaffolds possessed greater cellular size, area, perimeter, and lower cellular circularity compared to those in 4 wt% and 6 wt% CA scaffolds, suggesting a more prominent presence of cell clusters in softer substrates compared to stiffer substrates. The results also showed cells in 6 wt% CA having a higher average cell migration speed compared to those in 2 wt% and 4 wt% CA scaffolds, indicating a positive relationship between substrate stiffness and cell migration velocity. Findings from this experiment may contribute to the development of enhanced in vitro 3D breast tumor models for basic cancer research using 3D porous biomaterial scaffolds.

The intent of this thesis is to lay groundwork for examining the relationship between seismocardiography (SCG) and phonocardiography (PCG). Both are methods of measuring and describing heart mechanical function. SCG is described as chest vibrations while the heart beats, and PCG is described as acoustic chest surface signal believed to represent the heart valves opening or closing. SCG and PCG have both been used separately in clinical and research settings, but there is currently no clear comparison between the two. Therefore, there has been no way at the present to understand how one signal might inform the other. This study is an effort to fill that gap. SCG and PCG sensors were placed on subjects’ chests while sensor output was simultaneously recorded. The magnitudes of the signals and their trends were then compared against each other to see their similarities and differences. The comparisons demonstrated similar trends between the two sensor types, supporting the hypothesis that there is a relationship between the two that requires further research and insight.

<p> Musculoskeletal injuries are one of the leading causes of human disability. Tissue engineers aim to restore damaged musculoskeletal tissues by creating scaffolds that promote cellular adhesion, proliferation, and eventual differentiation into functional tissue. It is known that the extracellular matrix (ECM) regulates cellular behavior and is often used as a basis for biological scaffolds; however, current scaffolds often mimic the ECM of adult, homeostatic tissue and frequently lead to poor tissue restoration. What is rarely taken into consideration is that the ECM undergoes extensive remodeling during development to facilitate growth.</p> <p>In the musculoskeletal system, myogenic progenitors (<em>Pax3</em>+) and connective tissue cells (<em>Prx1</em>+) proliferate and differentiate into muscle, tendon, cartilage, and conjoining interfaces (<em>e.g.</em> myotendinous junction), while depositing and remodeling the ECM. As tissues mature, cells continue to refine ECM networks to withstand the functional demands to facilitate movement. The ECM composition and architecture of adult musculoskeletal tissues have been studied individually and are thought to be distinct; however, there has yet to be a comprehensive comparative analysis of the ECM in adult muscle, tendon, and the myotendinous junction (MTJ) in a single study. Additionally, how the matrisome of adult musculoskeletal system compares to the ECM dynamics during forelimb development, remain largely unknown due to lack of techniques to analyze embryonic matrisome composition and synthesis. </p> <p>To address these research gaps, we (1) used quantitative proteomics to map the matrisome composition in the mature murine MTJ, relative to the tendon and muscle; (2) adapted tissue fractionation and biorthogonal non-canonical amino acid tagging techniques to embryonic tissues as a method to quantify the global and nascent embryonic matrisome; and (3) subsequently used these techniques to establish a baseline of ECM dynamics during forelimb morphogenesis (embryonic day, E11.5-E14.5) and growth (postnatal day, P3 and P35). We hypothesized that proteomic evaluation of ECM composition and synthesis in developing and adolescent limbs would resolve differences between embryonic and growing tissues. Indeed, we saw significant differences in global and nascent matrisome composition between embryonic and adolescent forelimbs. Notably, the relative abundance and ratios of collagens associated with type I fibrillogenesis (I, III, and V) were significantly different as a function of development embryogenesis and across the adult muscle, MTJ, and tendon.</p> <p>Type I collagen fibrils are critical for tissue architecture and function. Using genetic mouse models, the regulatory roles of COL5A1 in the initiation of type I collagen fibrillogenesis, and organization of subsequent fibrils, have been well characterized in tendons and ligaments; however, is it unknown which cell types contribute COL5A1 to the ECM in the forelimb. To identify the functional contribution of COL5A1 by myogenic or connective tissue cell populations, we generated conditional (cre-flox) knock-out mouse models to inactivate <em>Col5a1</em> using <em>Pax3</em>- or <em>Prx1</em>-drivers, respectively. Haploinsufficiency of <em>COL5A1</em> in humans is associated classical Ehlers-Danlos syndrome, characterized by skin fragility and join instability; similar, albeit more severe, phenotypes were present in <em>Prx1Cre/+;Col5a1fl/fl</em> mutants, but not in <em>Pax3Cre/+;Col5a1fl/fl</em> mutants or controls. Interestingly, THBS4+ and COL22A1+ networks at the MTJ were morphologically affected in <em>Prx1Cre/+;Col5a1fl/fl</em> limbs. Additional work needs to be conducted to characterize the systematic phenotypes observed in <em>Prx1Cre/+;Col5a1fl/fl</em> limbs.</p> <p>Together, our results indicate that there are distinct, complex ECM dynamics, originating from distinct cell-types, that drive musculoskeletal morphogenesis in the forelimb. Further, the tools developed here will serve as a foundation for quantitative proteomic analyses of the matrisome composition in embryonic tissues. Collectively, this work provides a baseline of ECM protein dynamics during musculoskeletal morphogenesis, a helpful guide for tissue engineers in designing scaffolds to promote restoration of damaged tissues, with enhanced integration into the host tissue.</p>

<p dir="ltr">The goal of this dissertation is to develop precision technologies to facilitate establishing, in the context of stochastic organismal dynamics, organizational principles that govern basic regulatory processes in living systems. We focus on biological timekeeping, the interplay of biological lengths and timescales, and strategies governing the control of rapid vs. precise adaptation to changing phenomena supporting complex phenotypes. In particular, individual cells of unicellular organisms respond with remarkable precision and plasticity in their growth and division to changes in their noisy environments. Cells rely on scalable timekeepers and quantitative tradeoffs to accomplish this precision. In this dissertation we will address longstanding open questions in cell biology, such as: How does an individual cell maintain size homeostasis across multigenerational dynamics, as it repeatedly grows and divides? How does an organism adapt its growth rate to reflect changing environmental conditions? The development of understanding of systems-level organizational principles in a controlled experimental system in turn advances our general ability to predict and control stochastic organismal dynamics, and thus develop functional synthetic adaptive systems.</p>

Stroke is a chronic illness which often impairs survivors for extended periods of time, leaving the individual limited in motor function. The ability to perform daily activities (ADL) is closely linked to motor recovery following a stroke. The objective of this work is to employ surface electromyography (sEMG) gathered through a novel, wearable armband sensor to monitor and quantify ADL performance. The first contribution of this work seeks to develop a relationship between sEMG and and grip aperture, a metric tied to the success of post-stroke individuals’ functional independence. The second contribution of this work aims to develop a deep learning model to classify RTG movements in the home setting using continuous EMG and acceleration data. In contribution one, ten non-disabled participants (10M, 22.5 0.5 years) were recruited. We performed a correlation analysis between aperture and peak EMG value, as well as a one-way non parametric analysis to determine cylinder diameter effect on aperture. In contribution two, one non-disabled participant is instructed to wash a set of dishes. The EMG and acceleration data collected is input into a recurrent neural network (RNN) machine learning model to classify movement patterns. The first contribution’s analysis demonstrated a strong positive correlation between aperture and peak EMG value, as well as a statistically significant effect of diameter (p < 0.001). The RNN model built in contribution two demonstrated high capability at classifying movement at 94% accuracy and an F1-score of 86%. These results demonstrate promising feasibility for long-term, in-home classification of daily tasks. Future applications of this approach should consider extending the procedure to include post-stroke individuals, as this could offer valuable insight into motor recovery within the home setting.