Localization inside the human body using radio frequency (RF) transmission is gaining importance in a number of applications such as Wireless Video Capsule Endoscopy. The accuracy of RF localization depends on the technology adopted for this purpose. The two most common RF localization technologies use received signal strength (RSS) and time-of-arrival (TOA). This research presents a comparison of the accuracy of TOA and RSS based localization inside human tissue using computational techniques for simulation of radio propagation inside human tissues. Computer simulation of the propagation of radio waves inside the human body is extremely challenging and computationally intensive. We designed a basic, MATLAB coded, finite difference time-domain (FDTD) for the radio propagation in and around the human body and compared the results obtained from this software with the commonly used and commercially available Finite Element Method (FEM) modeling in ANSYS HFSS. We first show that the FDTD analysis yields comparable results. Then we use the software to simulate the RSS and TOA of the wideband signals propagated inside the human body for RF localization to compare the accuracies of the two methods. We then develop a statistical TOA model using empirical data gathered from these simulations; and, in conjunction with pre-established mathematical models for RSS, we compare the accuracy of each technique with the Cramer-Rao Lower Bound (CRLB) commonly used for calculation of bounds for the performance of localization techniques and the effects of human body movements.

abstract: Mobile apps have improved human lifestyle in various aspects ranging from instant messaging to tele-health. In the current app development paradigm, apps are being developed individually and agnostic of each other. The goal of this thesis is to allow a new world where multiple apps communicate with each other to achieve synergistic benefits. To enable integration between apps, manual communication between developers is needed, which can be problematic on many levels. In order to promote app integration, a systematic approach towards data sharing between multiple apps is essential. However, current approaches to app integration require large code modifications to reap the benefits of shared data such as requiring developers to provide APIs or use large, invasive middlewares. In this thesis, a data sharing framework was developed providing a non-invasive interface between mobile apps for data sharing and integration. A separate app acts as a registry to allow apps to register database tables to be shared and query this information. Two health monitoring apps were developed to evaluate the sharing framework and different methods of data integration between apps to promote synergistic feedback. The health monitoring apps have shown non-invasive solutions can provide data sharing functionality without large code modifications and manual communication between developers. / Dissertation/Thesis / M.S. Computer Science 2014

<div> Recent advancements of health monitoring sensing technologies are enabling plethora of new applications in a variety of biomedical areas. In this work, we present a new sensing technology that enables a fully autonomous monitoring of urinary tract infection (UTI). UTI is the second most common infection in the human body caused by bacterial pathogens, and costs millions of dollars each year to the patients and the health care industry. UTI is easily treatable using antibiotics if identified in early stages. However, when early stage identification is missed, UTI can be a major source of serious complications such as ascending infections, loss of kidney function, bacteremia, and sepsis. Unfortunately, the limitations of existing UTI monitoring technologies such as high cost, time-intensive sample preparation, and relatively high false alarm rate prohibit reliable detection of UTI in early stages. The problem becomes more serious in certain patient groups such as infants and geriatric patients suffering from neurodegenerative diseases, who have difficulties in realizing the symptoms and communicating the symptoms with their caregivers. In addition to the aforementioned difficulties, the fact that UTI is often asymptomatic makes early stage identifications quite challenging, and the reliable monitoring and detection of UTI in early stages remain as a serious problem.</div><div> To address these issues, we propose a diaper-embedded, self-powered, and fully autonomous UTI monitoring sensor module that enables autonomous monitoring and detection of UTI in early stages with minimal effort. The sensor module consists of a paper-based colorimetric nitrite sensor, urine-activated batteries, a boost dc-dc converter, a low-power sensor interface utilizing pulse width modulation, a Bluetooth low energy module for wireless transmission, and a software performing calibration at run-time. </div><div> To further optimize the sensor module, a new fully integrated DC-DC converter with low-profile and low ripple is developed. The proposed DC-DC converter maintains an extremely low level of output voltage ripples in the face of different battery output voltages, which is crucial for realizing low-noise sensor interfaces. Since the DC-DC converter is a part of a module embedded into a diaper, it is highly desirable for the DC-DC converter to have a small physical form factor in both area and height. To address this issue, the proposed DC-DC converter adopts a new charge recycling technique that enables a fully integrated design without utilizing any off-chip components. In addition, the DC-DC converter utilizes sub-module sharing techniques – multiple modules share a voltage buffer and a recycle capacitor to reduce power consumption and save chip area. The DC-DC converter provides a regulated voltage of 1.2V and achieves a maximum efficiency of 80% with a 300ohm load resistance. The output voltage ripple is in the range of 19.6mV to 26.6mV for an input voltage ranging from 0.66 to 0.86V.<br></div>