Doctor of Philosophy / Department of Computing and Information Sciences / David Gustafson / We use probabilistic graphs to predict the location of swarms over 100 steps in simulations in grid worlds. One graph can be used to make predictions for worlds of different dimensions. The worlds are constructed from a single 5x5 square pattern, each square of which may be either unoccupied or occupied by an obstacle or a target. Simulated robots move through the worlds avoiding the obstacles and tagging the targets. The interactions between the robots and the robots and the environment lead to behavior that, even in deterministic simulations, can be difficult to anticipate. The graphs capture the local rate and direction of swarm movement through the pattern. The graphs are used to create a transition matrix, which along with an occupancy matrix, can be used to predict the occupancy in the patterns in the 100 steps using 100 matrix multiplications. In the future, the graphs could be used to predict the movement of physical swarms though patterned environments such as city blocks in applications such as disaster response search and rescue. The predictions could assist in the design and deployment of such swarms and help rule out undesirable behavior.

This thesis explores a methodology to quantify trust in wearable medical devices (WMD) by addressing two main challenges: identifying key factors influencing trust and developing a formal framework for precise trust quantification under uncertainty. The work empirically validates trust factors and uses a Bayesian network to quantify trust. The thesis further employs a data-driven approach to estimate Bayesian parameters, facilitating query-based inference and validating the trust model with real and synthetic datasets, culminating in a customizable parameterized trust evaluation prototype for WMD. / Advances in sensor and digital communication technologies have revolutionized the capabilities of wearable medical device (WMD) to monitor patients’ health remotely, raising growing concerns about trust in these devices. There is a need to quantify trust in WMD for their continued acceptance and adoption by different users. Quantifying trust in WMD poses two significant challenges due to their subjective and stochastic nature. The first challenge is identifying the factors that influence trust in WMD, and the second is developing a formal framework for precise quantification of trust while taking into account the uncertainty and variability of trust factors. This thesis proposes a methodology to quantify trust in WMD, addressing these challenges. In this thesis, first, we devise a method to empirically validate dominant factors that influence the trustworthiness of WMD from the perspective of device users. We identified the users’ awareness of trust factors reported in the literature and additional user concerns influencing their trust. These factors are stepping stones for defining the specifications and quantification of trust in WMD. Second, we develop a probabilistic graph using Bayesian network to quantify trust in WMD. Using the Bayesian network, the stochastic nature of trust is viewed in terms of probabilities as subjective degrees of belief by a set of random variables in the domain. We define each random variable in the network by the trust factors that are identified from the literature and validated by our empirical study. We construct the trust structure as an acyclic-directed graph to represent the relationship between the variables compactly and transparently. We set the inter-node relationships, using the goal refinement technique, by refining a high-level goal of trustworthiness to lower-level goals that can be objectively implemented as measurable factors. Third, to learn and estimate the parameters of the Bayesian network, we need access to the probabilities of all nodes, as assuming a uniform Gaussian distribution or using values based on expert opinions may not fully represent the complexities of the factors influencing trust. We propose a data-driven approach to generate priors and estimate Bayesian parameters, in which we use data collected from WMD for all the measurable factors (nodes) to generate priors. We use non-functional requirement engineering techniques to quantify the impacts between the node relationships in the Bayesian network. We design propagation rules to aggregate the quantified relationships within the nodes of the network. This approach facilitates the computation of conditional probability distributions and enables query-based inference on any node, including the high-level trust node, based on the given evidence. The results of this thesis are evaluated through several experimental validations. The factors influencing trust in WMD are empirically validated by an extensive survey of 187 potential users. The learnability, and generalizability of the proposed trust network are validated with a real dataset collected from three users of WMD in two conditions, performing predefined activities and performing regular daily activities. To extend the variability of conditions, we generated an extensive and representative synthetic dataset and validated the trust network accordingly. Finally, to test the practicality of our approach, we implemented a user-configurable, parameterized prototype that allows users of WMD to construct a customizable trust network and effectively compare the trustworthiness of different devices. The prototype enables the healthcare industry to adapt and adopt this method to evaluate the trustworthiness of WMD for their own specific use cases. / Thesis / Doctor of Philosophy (PhD) / In this thesis, two challenges in quantifying trust in wearable medical devices, are addressed. The first challenge is the identification of factors influencing trust which are inherently subjective and vary widely among users. To address this challenge, we conducted an extensive survey to identify and validate the trust factors. These factors are stepping stones for defining the specifications and quantifying trust in wearable medical devices. The second challenge is to develop a precise method for quantification of trust while taking into account the uncertainty and variability of trust factors. We constructed a Bayesian network, that captures the complexities of trust as probabilities of the trust factors (identified from the survey) and developed a data-driven approach to estimate the parameters of the Bayesian network to compute the measure of trust. The findings of this thesis are empirically and experimentally validated across multiple use cases, incorporating real and synthetic data, various testing conditions, and diverse Bayesian network configurations. Additionally, we developed a customizable, parameterized prototype that empowers users and healthcare providers to effectively assess and compare the trustworthiness of different wearable medical devices.