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This work describes the development of a device for measuring CO2 in breath, which has applications in monitoring a variety of health issues, such as Chronic Obstructive Pulmonary Disease (COPD), asthma, and cardiovascular disease. The device takes advantage of colorimetric sensing technology in order to maintain a low cost and high user-friendliness. The sensor consists of a pH dye, reactive element, and base coated on a highly porous Teflon membrane. The transmittance of the sensor is measured in the device via a simple LED/photodiode system, along with the flow rate, ambient relative humidity, and barometric pressure. The flow is measured by a newly developed flow meter described in this work, the Confined Pitot Tube (CPT) flow meter, which provides a high accuracy with reduced flow-resistance with a standard differential pressure transducer. I demonstrate in this work that the system has a high sensitivity, high specificity, fast time-response, high reproducibility, and good stability. The sensor has a simple calibration method which requires no action by the user, and utilizes a sophisticated, yet lightweight, model in order to predict temperature changes on the sensor during breathing and track changes in water content. It is shown to be effective for measuring CO2 waveform parameters on a breath-by-breath basis, such as End-Tidal CO2, Alveolar Plateau Slope, and Beginning Exhalation Slope.
ContributorsBridgeman, Devon (Author) / Forzani, Erica S (Thesis advisor) / Nikkhah, Mehdi (Committee member) / Holloway, Julianne (Committee member) / Raupp, Gregory (Committee member) / Emady, Heather (Committee member) / Arizona State University (Publisher)
Created2017
Description
Behavior-driven obesity has become one of the most challenging global epidemics since the 1990s, and is presently associated with the leading causes of death in the U.S. and worldwide, including diabetes, cardiovascular disease, strokes, and some forms of cancer. The use of system identification and control engineering principles in the design of novel and perpetually adaptive behavioral health interventions for promoting physical activity and healthy eating has been the central theme in many recent contributions. However, the absence of experimental studies specifically designed with the purpose of developing control-oriented behavioral models has restricted prior efforts in this domain to the use of hypothetical simulations to demonstrate the potential viability of these interventions. In this dissertation, the use of first-of-a-kind, real-life experimental results to develop dynamic, participant-validated behavioral models essential for the design and evaluation of optimized and adaptive behavioral interventions is examined. Following an intergenerational approach, the first part of this work aims to develop a dynamical systems model of intrauterine fetal growth with the prime goal of predicting infant birth weight, which has been associated with subsequent childhood and adult-onset obesity. The use of longitudinal input-output data from the “Healthy Mom Zone” intervention study has enabled the estimation and validation of this fetoplacental model. The second part establishes a set of data-driven behavioral models founded on Social Cognitive Theory (SCT). The “Just Walk” intervention experiment, developed at Arizona State University using system identification principles, has lent a unique opportunity to estimate and validate both black-box and semiphysical SCT models for predicting physical activity behavior. Further, this dissertation addresses some of the model estimation challenges arising from the limitations of “Just Walk”, including the need for developing nontraditional modeling approaches for short datasets, as well as delivers a new theoretical and algorithmic framework for structured state-space model estimation that can be used in a broader set of application domains. Finally, adaptive closed-loop intervention simulations of participant-validated SCT models from “Just Walk” are presented using a Hybrid Model Predictive Control (HMPC) control law. A simple HMPC controller reconfiguration strategy for designing both single- and multi-phase intervention designs is proposed.
ContributorsFreigoun, Mohammad T (Author) / Raupp, Gregory B (Thesis advisor) / Tsakalis, Konstantinos S (Thesis advisor) / Spanias, Andreas S (Committee member) / Forzani, Erica S (Committee member) / Muhich, Christopher L (Committee member) / Arizona State University (Publisher)
Created2021