Graduation Year

2024

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Electrical Engineering

Major Professor

Wilfrido A. Moreno, Ph.D.

Committee Member

Ismail Uysal, Ph.D.

Committee Member

Srinivas Katkoori, Ph.D.

Committee Member

Chung Seop Jeong, Ph.D.

Committee Member

Balaji Ramadoss, Ph.D.

Keywords

Digital Transformation, Intelligent, Agile MBSE, Computer Vision, Design Thinking, Edge

Abstract

Healthcare patient monitoring is undergoing a significant digital transformation, and the integration of Cyber-Physical Systems (CPS) and Artificial Intelligence (AI) is becoming increasingly crucial in reshaping patient care. In an era where digital technology is revolutionizing medical practices, this research aims to take a leading role in advancing a fundamental aspect of predictive and sustainable healthcare practices, enhancing patient outcomes and uplifting the practice of medicine.

This research focuses on the study of Digital Twins for precision health, which are designed to monitor and provide intricate, personalized feedback dynamically during a patient's healthcare experience. The architecture of the system is to create a synergy between a bedside patient monitoring setup and AI tools to provide advanced capabilities to healthcareprofessionals. The philosophy of this research is empowered by three key principles: Design Thinking, Model Based Systems Engineering (MBSE), and Agile development methodology that collectively facilitate the process of digital transformation in the healthcare sector. These three proposed methodologies foster iterative problem-solving, promote holistic understanding, and enhance adaptability in complex project environments.

This dissertation aims to innovate the methods of designing a bedside patient monitoringsystem, propelled by digital innovation. The cornerstone of this research lies in the integration of design strategies that can improve the performance of the patient monitoring system thereby contributing to build the next generation patient monitoring digital tools that support nurses and patient's outcomes. This research integrates Bedside Patient Pose Classification (BPPC), which includes Supine, Left, Right poses. This integration is designed to monitor the BPPC system efficiently and incorporates both connectivity tools and AI from cyber-physical perspective. The bedside patient monitoring system acts as a central node, not only facilitating computer vision data acquisition and processing but also enabling real-time communication with healthcare providers.

Download

Share

COinS