The potential of Digital twin (DT) technology outside of the industrial field has been recognized by researchers who have promoted the vision of applying DTs technology beyond manufacturing, to purposes such as enhancing human well-being and improving quality of life (QoL). The expanded definition of DTs to incorporate living and nonliving physical entities into the definition of DTs was a key motivation behind the model introduced in this thesis for building health digital twins of citizens. In contrast with DTs that have been developed in more industrial fields, this type of digital twins modeling necessitates protecting each citizen's unique identity while also representing features common to all citizens in a unified way. In nature, DNA is an example of a model that is both unified, common to all humans, and unique, distinguishing each human as an individual. DNA’s architecture is what inspired us to propose a digital twin DNA (DT-DNA) model as the basis for building health DTs for citizens. A review of the literature shows that no unified model for citizens’ health has been developed that can act as a base for building digital twins of citizens while also protecting their unique identity thus we aim to fill this gap in this research. Accordingly, in this thesis, we proposed a DT-DNA model, which is specifically designed to protect the unique identity of each citizen’s digital twin, similar to what DNA does for each human. We also proposed a DT-DNA-based framework to build standardized health digital twins of citizens on micro, meso and macro levels using two ISO standards: ISO/IEEE 11073 (X73) and ISO 37120. To achieve our goal, we started by analyzing the biological DNA model and the influencing factors shaping health in smart cities. The purpose of the first is to highlight the DNA model features which provide the building blocks for our DT-DNA model. The purpose of the latter is to determine the main bases of our DT-DNA model of health DTs. Based on the analysis results; we proposed DT-DNA to model health DTs for citizens. In keeping with our DNA analogy, we have identified four bases, A, T, G, and C, for our unified and unique DT-DNA model. The A base in the proposed model represents a citizen’s anthropometric when we build the DT-DNA on an individual level and represents the city’s regulatory authorities when we build the DT-DNA on community and city levels. The T base represents different tasks included in the provided health data that are required to model citizens’ health DT-DNA on different levels. The G base represents the geographic and temporal information of the city, where the citizen exists at the time of data collection. The C base represents the context at the time of data collection. To proof the concept, we present our initial work on building health DTs for citizens in four case studies. The first two case studies are dedicated for health DTs at the micro level, the third case study is dedicated for health DTs at the meso level and the fourth case study is dedicated for health DTs at the macro level. In addition, we developed an algorithm to compare cities in terms of their community fitness and health services status. The four case studies provide promising results in terms of applicability of the proposed DT-DNA model and framework in handling the health data of citizens, communities and cities, collected through various sources, and presenting them in a standardized, unique model.

<p>There are no commercially available wearable shoe in-sole sensors that are capable of measuring and distinguishing between shear and compression forces. Companies have already developed shoe sensors that simply measure pressure and make general inferences on the collected data with elaborate software [2, 3, 4, 5]. Researchers have also attempted making sensors that are capable of measuring shear forces, but they are not well suited for biomedical applications [61, 62, 63, 64]. This work focuses on the development of a novel wearable sensor system that is capable of identifying and measuring shear and compression forces through the use of capacitive sensing. Custom hardware and software tools such as materials test systems and capacitive measurement systems were developed during this work. Numerous sensor prototypes were developed, characterized, and optimized during the scope of this project. Upon analysis of the data, the best capacitive measurement system developed in this work utilized the CAV444 IC chip, whereas the use of the Arduino-derived measurement system required data filtering using median and Butterworth zero phase low pass filters. The highest dielectric constant reported from optimization experiments yielded 9.7034 (+/- 0.0801 STD) through the use of 60.2% by weight calcium copper titanate and ReoFlex-60 silicone. The experiments suggest certain sensors developed in this work feasibly measure and distinguish between shear and compressional forces. Applications for such technology focus on improving quality of life in areas such as managing diabetic ulcer formation, preventing injuries, optimizing performance for athletes and military personnel, and augmenting the scope of motion capture in biomechanical studies.</p>