Back pain and injury are a global health issue and are a leading cause of work and activity absence. Prevention would not only save those affected from the burden of pain and discomfort, but would also save people from loss of over 290 million workdays annually and save the healthcare system billions of dollars in expenses per year. Successful research and development of a wearable technology capable of comprehensively monitoring spinal postures that are leading causes of back pain and injury can result in prevention of mild to severe back pain and injury for high-risk people. To accomplish this, the Smart Spine Tape is being developed with specific focus on accuracy, usability, and accessibility, all of which are important factors to consider when engineering for a wide array of populations. Accuracy was assessed using three human participants, with spinal angle data of the Smart Spine Tape being compared to established motion analysis technology data. Prototypes of the device showed promise in the ability to accurately measure spinal postures, but inconsistencies between samples and trials indicated that further development is necessary. Usability and accessibility were assessed using ten human participants who completed one workout each and reported on the tape’s comfort, durability, and ease of use, as well as their thoughts on how much they would be willing to pay for a fully functional version of the device. Participants reported high comfort, high durability, and moderate ease of use throughout their experiences, with the average price range that they would be willing to pay being between $25 and $75. Future directions have been identified that address inconsistencies in data collected by the Smart Spine Tape, possibly caused by inconsistent resistive properties of the piezoresistive ink and plastic deformation of the tape during testing. These future directions involve modifying testing, material, and fabrication methods.

A study was performed on a recently developed prototype of the Yong-Geddes surrogate arm design to collect compliance data of the various system components and determine the accuracy of measurements made through the bench top model. The study was performed to perceive the effectiveness of the model as a tool for validating non-invasive blood pressure detection monitors. Three stages of testing were performed to gather pressure and volume data from an artificial artery component, a sphygmomanometer, and the surrogate arm system to produce compliance estimations. Mathematical equations from supported arterial hemodynamics studies and clinical trials were applied to the pressure and volume data. Dr. Drzewiecki’s equation for arterial compliance was capable of predicting the region of the highest compliance of the artificial artery and produced an overall value of 38.81% for the data. A second degree inverse polynomial was developed and modeled the sphygmomanometer compliance measurements with a of 99.09%. Significant error was observed throughout all stages of the compliance testing, which was attributed to factors such as excessive noise due to faulty data collection equipment and irreparable leaks in the fluid flow system.

The clinical usage of Seismocardiography (SCG) is increasing as it is being shown to be an effective non-invasive measurement for heart monitoring. SCG measures the vibrational activity at the chest surface and applications include non-invasive assessment of myocardial contractility and systolic time intervals. Respiratory activity can also affect the SCG signal by changing the hemodynamic characteristics of cardiac activity and displacing the position of the heart. Other clinically significant information, such as systolic time intervals, can thus manifest themselves differently in an SCG signal during inspiration and expiration. Grouping SCG signals into their respective respiratory cycle can mitigate this issue. Prior research has focused on developing machine learning classification methods to classify SCG events as according to their respiration cycle. However, recent research at the Biomedical Acoustics Research Laboratory (BARL) at UCF suggests grouping SCG signals into high and low lung volume may be more effective. This research aimed at com- paring the efficiency of grouping SCG signals according to their respiration and lung volume phase and also developing a method to automatically identify the respiration and lung volume phase of SCG events.

No description available.

Cardiovascular disease is the leading cause of mortality in diabetic patients, and diabetes is one of the main causes of cardiovascular disease. Risk factors for cardiovascular disease result in structural and functional changes in the vascular wall. Arterial stiffness is a prominent structural change observed in the arterial wall that can be measured in clinical settings. The purpose of this thesis was to create a static model of the changes in arterial stiffness seen in diabetes. Elastic tubes with varying wall thicknesses were used to create artificial arteries for this purpose. Compliance (inverse of stiffness) of the arteries was determined using a pressurevolume model and a mathematical model. The compliance curves generated using the pressurevolume model exhibited trends predicted by the mathematical model. These trends were comparable to arterial stiffness changes seen in diabetes. Compliance obtained from pressurevolume measurements of elastic tubes with varying wall thickness can therefore be used to model the general trends of arterial stiffness in diabetes.

<p>The work in this thesis deals with pre-clinical development of rapid in vivo ³¹P mag- netic resonance spectroscopy (MRS) techniques. Current MRI literature of ³¹P spec- troscopy presents evidence of increased concentrations of phosphomonoesters (PME), and phosphodiester (PDE) as well as inorganic phosphate concentrations in tumor tissue. Human breast cancer studies have demonstrated correlation between disease progression and both PME and PDE peaks. Furthermore, ³¹P MRS can be used to detect, grade tumours and monitor response to chemo and radiation therapy.<br />Tumor measurements are typically static (i.e. single measurement per scan). In other experiments, on muscle for example, dynamic measures are required the purpose of which is to assess temporal function and recovery. In all ³¹P acquisitions there are problems surrounding RF coil design, pulse sequence speed, localization and system calibration. The work presented here focused on improving all these aspects and provide easy and reliable work flow to use ³¹P MRS in a clinical setting.<br />One of the aspects of this thesis lies in designing and construction of an RF coil that is well suited for integration with a clinical MRI breast imaging and biopsy system. The designed coil was tuned for simultaneous operation at ³¹P (51.73 MHz) and ¹H (127.88MHz) Larmor frequencies. This design has advantages in the fact that complex pulse sequences with heteronuclear decoupling could be performed easily. The additional features of the coil design is that it is possible to swap it into the breast imaging system without moving the patient. Along with the designed coil, custom software was written to assist with transmit gain calibration of ³¹P RF pulses, to ensure maximum MR signal. The automated prescan ensures easy work flow and minimizes the operator variability and patient time inside the MR scanner.<br />Another aspect of this thesis deals with rapid pulse sequence development, to further speed up the ³¹P MRS data acquisition. Echo planar spectroscopic imaging (EPSI) with a fly–back gradient trajectory is currently one of the most reliable and robust techniques for speeding up chemical shift imaging (CSI) acquisitions. A ³¹P EPSI sequence was written to acquire spectroscopic imaging data at 1, 2 and 2.6 cm spatial resolution and spectral bandwidth of 3125 Hz. The sequence showed an ability to speed up data acquisition up to 16 times, where SNR permits.<br />Phantom studies were used to verify the double tuned coil and EPSI sequence en- suring proper and safe operation. In vivo measurements of an exercising muscle demonstrated the ability of ³¹P EPSI to play an important role in rapidly acquiring spatially localized ³¹P spectroscopic data.<br />With these preclinical developments in place a clinical trial is possible using ³¹P MRS rapidly and efficiently. Furthermore the increased usability of ³¹P MRS provided by the tools developed in this thesis can prove to be beneficial by integrating ³¹P MRS into existing clinical protocols.</p> / Doctor of Science (PhD)

<p>Sight-threatening diseases, such as age-related macular degeneration (AMD), affect the tissues of the posterior segment of the eye. Though modern classes of biomolecular based drugs are therapeutically useful, drug targeting for prolonged bioavailability to pathological sites within the eye is challenging. Current delivery approaches are invasive and lack control over drug release rates and tissue-specific localization. In this thesis, a device using microneedles embedded in a flexible platform was developed that could potentially overcome these challenges.</p> <p>New methods for microneedle fabrication were developed by co-opting simple chemical etch methods commonly used for optical probe fabrication as an alternative to current complex and expensive photolithographic technologies to produce out-of-plane, high aspect ratio microneedles which are often constrained materially to silicon and metal. Microneedles with repeatable tip and taper sizes were obtained using hydrofluoric acid, an organic phase and fused-silica capillary tubing. Microneedles with 10 um tips were made using single and batch mode methods and were then integrated into poly (dimethylsiloxane) (PDMS) for alignment using low cost micromolding approaches offering the same degree of accuracy provided by conventional photolithography<strong>. </strong></p> <p>Single microneedle-based devices successfully delivered rhodamine intrasclerally, intravitreally, suprachoroidally and to the retina. This is the first demonstration of active delivery to specific spatial regions within the posterior eye at controllable rates using a non-implantable, biocompatible device – with minimal fabrication facilities, equipment and cost. The fabricated device demonstrated a new hybrid approach of coupling a rigid microneedle with a soft and pliable substrate that could conform to biological tissues.</p> / Doctor of Philosophy (PhD)

<p>Minimally invasive operations have gained popularity over open surgical procedures in the recent years. These procedures, require the surgeon to perform highly specialized tasks including manipulation of tools through small incisions on the surface of the skin while looking at the images that are displayed on a screen. Therefore, effective training is required for the surgeons prior to performing such procedures on patients.</p> <p>In this thesis I explored a novel idea for creating a training system for arthroscopic surgery. Previously obtained CT images of a patient model and the surgical tools are manipulated to create a library of fluoroscopy images. The surgical tools are tracked (a mechanical tracker and an electromagnetic tracker used in each iterations) in order to generate a spacial relationship between the patient model and the surgical tools. The position and orientation information from the tracking system is translated into the image coordinate frame. These homologous points in the two images (of surgical tools and the patient model), are used to co-register and overlay the two images and create a virtual fluoroscopy image.</p> <p>The output image and the system performance was found to be very good and quite similar to that of a fluoroscopy system. The registration accuracy was evaluated using Root Mean Square Target Registration Error (RMS TRE). The RMS TRE for the system setup with the mechanical tracker was evaluated at 2:0 mm, 2:1 mm, and 2:5 mm, for 4, 5, and 6 control points, respectively. In the system setup with the electromagnetic tracking system the RMS TRE was evaluated at 7:6 mm, 12:4 mm, and 11:3 mm, for 5, 7, and 9 control points, respectively. The acceptable range of error for arthroscopy procedures has been proposed to be 1-2 mm.</p> <p>It was concluded that by using a tracking system, which is not prone to interference and allows for a wide range of motion this system can be completed to the point of manufacturing and use in training new surgeons.</p> / Master of Applied Science (MASc)

The Tissue Engineering Lab at California Polytechnic State University San Luis Obispo focuses on creating tissue-engineered Blood Vessel Mimics (BVMs) designed for the preclinical testing of neurovascular devices. These BVMs are composed of silicone models, representing anatomically accurate neurovasculatures, that are sodded with vascular cell types and then cultivated in bioreactors (which maintain physiologic conditions). These silicone models are currently sourced externally from industry partners, so the primary goal of this thesis was to develop the means and methods for the Tissue Engineering Lab to manufacture silicone models in-house. The first aim of this thesis was to develop and explore injection molding as a possible technique for manufacturing silicone models; this included prototyping various designs of molds, developing a viable workflow for injection molding, then assessing the resulting silicone models through measurement characterization, cytotoxicity screening, and BVM set-ups. The first aim found that injection molding was a viable manufacturing technique for making silicone models. The second aim of this thesis explored an alternative manufacturing method, dip-casting, to produce silicone models. The development of dip-casting was similar to injection molding, where several prototyping stages resulted in a viable workflow for making silicone models; the resulting silicone models were then assessed via measurement characterization and a BVM set-up. The second aim found that, in addition to injection molding, dip-casting was a viable technique for making silicone models, although the overall morphology of the resulting models was less desirable than those made by injection molding. The third and final aim of this thesis compared both manufacturing techniques (i.e., injection molding and dip-casting); this aim established that injection molding was preferable for making simple (less intricate) silicone models, whereas dip-casting was preferable for producing complex (more intricate) silicone models. Although the dip-casting technique requires more development to capture complex shapes and produce models with desirable morphologies, the injection molding protocol was formalized into a prescribed workflow for the Tissue Engineering Lab to reference. Overall, this thesis developed and explored two different manufacturing techniques for making silicone models and found that both were capable of making silicone models that could be used to create tissue-engineered BVMs, with injection molded models being ready to implement as the dip-casting process continues to be refined.

Physical activity plays a crucial role in maintaining overall health and reducing the risk of various chronic diseases. Step counting has emerged as a popular method for assessing physical activity levels, given its simplicity and ease of use. However, accurately measuring step counts in free-living environments presents significant challenges, with most activity trackers exhibiting a percent error above 20%. This study aims to address these challenges by creating a machine learning algorithm that leverages activity labels to improve step count accuracy in real-world conditions. Two approaches to balancing data were used: one employed a simpler oversampling technique, while the other adopted a more nuanced approach involving the removal of outliers. Models 1 and 2 were trained on each of these uniquely balanced datasets. Model 1 performed much better than Model 2 on testing datasets, but both achieved better than 20% error on new datasets, indicating their potential for more accurate step counting in real-world conditions. Despite challenges such as data imbalance, the study demonstrated the viability of using activity labels to enhance step counting accuracy. Future research should focus on addressing data imbalances and exploring more advanced machine learning techniques for more reliable activity monitoring.