Date of Award

2022

Degree Type

Dissertation

Degree Name

Doctor of Philosophy in Electrical Engineering

Department

Electrical, Computer, and Biomedical Engineering

First Advisor

Kunal Mankodiya

Abstract

The world is witnessing a rising number of preterm infants who are at significant risk of medical conditions. This situation demands innovative solutions for medical monitoring in the Neonatal Intensive Care Unit (NICU). Medical parameters such as heart rate (HR), respiration rate (RR), and blood oxygen level (SpO2) are continuously monitored in premature infants in the NICU using a set of wired, sticky electrodes attached to the body. Even though medical adhesives are used, they are still harmful for preterm babies’ underdeveloped skin. Also, development of an accurate and sophisticated technology to monitor respiration rate is a necessity since the current respiration monitoring in NICU is extracted from the ECG sensors and the ECG sensors resulting in inaccurate measurement for respiration.

Motivated by these challenges, this doctoral dissertation focused on to design an adhesive-free, wireless, and wearable respiration monitoring system. The aim of this research is to combine smart e-textiles and Internet-of-Things (IoT) technologies to offer a novel approach of respiration monitoring technology which can be a promising solution for physiological signal monitoring in clinical settings such as NICU. To achieve this, a chest belt integrated with textile pressure sensors and an Inertial Measurement Unit (IMU) was created. The pressure sensors measure the changes in chest expansion and contraction during breathing and the IMU senses movement changes. The chest belt was connected to a a wearable embedded system (WES) and an edge computing device (ECD). The WES was programmed to collect pressure data from textile sensors and send this data to ECD via Wi-Fi. The software architecture in ECD was wirelessly connected to the WES and received pressure data. After that, feature extraction algorithms were applied to the pressure data to extract the respiration rate from the data. So, the respiration monitoring system took advantage of textile-based sensors and IoT technologies to monitor physiological parameters.

To evaluate the system under different conditions such as different sizes and shapes, three different chest belts were developed. The first iteration, e-Fabric Baby Guard was designed for babies. Due to the IRB obligations, it was tested on high fidelity, life-like baby mannequin which can be controlled for assesments in different breathing rates. In experimental protocol, different breathing rates (number of breaths per minute) and different kind of movements (i.e arm swing, diaper change) were simulated and tested. After getting promising results, the second iteration called NeoWear was designed. To collect real human data, NeoWear was designed as a one piece adult chest belt. The same WES, ECD and algorithms were used for data collection and feature extraction. After that, the third iteration, SolunumWear was designed as two pieces adult chest belt to cover whole chest area. The same WES, ECD and algorithms were used for data collection and feature extraction. Different postures (standing, sitting, bending) were added to existing experimental protocol to evaluate the effect of different postures on breathing. Those experiments validated by comparing with infrared cameras as gold standard. It was observed that the system was capable of sensing the changes in breathing rates and the effect of movements and postures on the breathing signal.

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