Abstract
Black twin infants in the United States die at more than twice the rate of white twins. A statistic that reflects both technological limitations and systemic inequities in maternal healthcare. This capstone project responds to that clinical challenge by developing a fetal heart rate monitoring system that can distinguish between heartbeats in multiple gestation pregnancies, where conventional Doppler ultrasound often fails. The goal is to reduce diagnostic errors and improve care for high-risk pregnancies, especially for marginalized communities. In parallel, the STS paper examines how structural racism, implicit bias, and uneven healthcare quality contribute to persistent racial disparities in maternal health outcomes. While the capstone introduces a technical solution to a specific clinical issue, the STS research broadens the lens, showing how even the best technologies can fall short without systemic change.
The capstone project targets a clear gap in existing fetal monitoring systems, which often cannot reliably isolate fetal heart signals during multiple gestation pregnancies. These limitations can lead to misdiagnosis or missed complications, particularly for patients in understaffed or resource-constrained settings. To address this, the team designed a hydrogel phantom simulating the uterine environment, embedding sound-emitting devices to mimic fetal heartbeats and sensors to detect them. The system used an Arduino Mega microcontroller and implemented a triangulation algorithm based on time difference of arrival to localize signals. This technical setup replicates conditions seen in clinical monitoring and serves as a platform for evaluating signal differentiation strategies in real time.
Experimental testing showed that the system could accurately isolate heart signals when frequencies were sufficiently distinct, with error margins under 3%. Localization performance was also strong under controlled conditions. However, when frequencies overlapped or fell into lower ranges, errors rose to nearly 25%, highlighting the system's limitations and the need for further refinement. These results suggest that the prototype holds promise for improving monitoring in multiple gestation pregnancies, though challenges remain in handling signal complexity. The technical report concludes that the device has potential clinical value but must be iterated upon before clinical deployment. Its success could improve fetal monitoring for high-risk patients and reduce dependence on operator skill.
The STS paper asks a broader question: How do systemic racism and implicit bias shape racial disparities in maternal health outcomes in the U.S.? Drawing from literature, public health data, and case studies, the research reveals that these disparities persist across all stages of maternal care—even among highly educated or insured Black women. Using an ethics of care framework, the paper emphasizes the need for relational, context-sensitive health care that addresses patients’ lived experiences. The methodology combines a literature review with case analysis to uncover how provider assumptions, resource gaps, and structural racism contribute to unequal outcomes. These factors create a landscape where Black women are more likely to suffer complications and receive substandard care, regardless of their socioeconomic background.
Evidence showed disparities in access to prenatal care, Cesarean rates, pain management, and postpartum follow-up, often driven by provider bias, institutional neglect, and geographic segregation in hospital quality. Studies confirmed that even technological advancements can fall short if healthcare systems remain inequitable. The research concludes that while tools like improved fetal monitors are valuable, real progress requires institutional reforms: standardized protocols, implicit bias training, expanded maternity coverage, and equitable technology distribution. Health equity can only be achieved through a dual commitment to engineering innovation and systemic accountability in care delivery.
