Engineering Commons^™

Advancing Omnimodality: Expanding Human Creativity Through Adaptable And Accessible Multimodal Computing Systems, Joshua Urban Davis

Dartmouth College Ph.D Dissertations

Emerging technologies have given us a whole host of new ways for people to be

creative. From the immersive worlds of AR/VR to the synthesis powers of largelanguage

models and generative AI, these new tools hold the potential to reshape

human expression and creativity. But how can we ensure that these new modalities

are accessible to everyone, even those who aren’t able bodied? This thesis advocates

for a human-centered approach to the development of many-modal systems. I will

probe how our machines support, direct, or inhibit creativity as a mode of problem

solving through 6 novel multimodal prototype interfaces and …

Heartfev1: A Mobile Electrocardiogram Based System For Inferring Forced Expiratory Volume In One Second From Patients With Chronic Obstructive Pulmonary Disease, Maria Nyamukuru

Dartmouth College Ph.D Dissertations

Chronic Obstructive Pulmonary Disease (COPD), characterized by chronic airway inflammation and airflow obstruction, is the third leading cause of death globally. Patients with COPD experience exacerbated symptoms like breathlessness and cough, significantly impacting their quality of life and leading to costly hospitalizations. Early detection of COPD exacerbations is crucial for mitigating these negative effects.

The most critical element for early detection of COPD exacerbations is daily monitoring of lung function, particularly forced expiratory volume in one second (FEV1), a key metric of lung function. By tracking declines in FEV1, COPD exacerbations can be predicted up to two weeks in advance, …

Surgical Navigation In Image-Guided Transoral Robotic Surgery, Yuan Shi

Dartmouth College Ph.D Dissertations

Transoral robotic surgery (TORS) is a minimally invasive approach in treating head and neck cancers and has demonstrated improved surgical outcomes with reduced morbidity when compared to traditional open surgery. However, the lack of haptic feedback makes it more difficult to assess the tumor extent and locate critical structures (e.g., artery and nerves). In standard-of-care TORS, preoperative imaging is used as guidance; however, it becomes inaccurate intraoperatively: the patient’s neck is hyper-extended, surgical instruments are introduced, and soft tissues deform significantly. As a result, the surgeon is required to mentally predict intraoperative deformation, which can lead to bleeding complications and …

A Political Theory Of Engineered Systems And A Study Of Engineering And Justice Workshops, Dominic David Carrese

Dartmouth College Master’s Theses

Since there are good reasons to think that some engineered systems are socially undesirable—for example, internal combustion engines that cause climate change, algorithms that are racist, and nuclear weapons that can destroy all life—there is a well-established literature that attempts to identify best practices for designing and regulating engineered systems in order to prevent harm and promote justice. Most of this literature, especially the design theory and engineering justice literature meant to help guide engineers, focuses on environmental, physical, social, and mental harms such as ecosystem and bodily poisoning, racial and gender discrimination, and urban alienation. However, the literature that …

Disentangling Cyclic Causality: An Instance-Based Framework For Causal Discovery, Chase A. Yakaboski

Dartmouth College Ph.D Dissertations

Correlation does not imply causation" is one of the fundamental principles taught in science, emphasizing that associations between variables do not necessarily indicate causality. Yet, over the past three decades, extensive research has begun to challenge this perspective by developing sophisticated methods to differentiate causal from correlative relationships. This research suggests that correlations often involve a blend of confounded and causal interactions, which, given certain assumptions, can be disentangled to uncover actionable insights and deepen our understanding of physical, biological, and societal systems.

Accurately discovering causal relationships from data amidst cyclic dynamics remains a challenging open problem in causality research. …