I-Corps: Translation Potential of Stress Monitoring for Maternity Using a Physiological Sensor Wearable Device

This I-Corps project focuses on the development of a stress monitoring device for women of reproductive age. Pregnancy and the postpartum period are times of major physical and emotional change, often bringing higher levels of stress that can lead to serious health problems such as preterm birth, low birth weight, and developmental delays in children. Currently, stress is usually measured through surveys or conversations with healthcare providers, which rely on self-reporting and may miss early warning signs. To address this need, the solution provides a simple wearable device that continuously monitors key health signals such as heart rate, sleep, movement, and skin responses. The device connects to a mobile application that uses artificial intelligence to analyze the data and provide real-time insights, alerts, and personalized recommendations to both the user and their care team. Approximately one in five women in the United States experiences mental health challenges during or after pregnancy, and maternal mortality has more than doubled in the past two decades—often due to stress-related cardiovascular and mental health conditions. By enabling earlier detection and targeted support, this solution helps users manage their health more effectively, improves outcomes for mothers and infants, and reduces costly complications for healthcare systems and employers.

This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a wearable device integrated with a suite of advanced physiological sensors, including continuous glucose monitors, blood pressure cuffs, electrodermal activity sensors, photoplethysmography sensors for heart rate variability, accelerometers for physical activity tracking, and sleep monitors. The device is designed to capture a broad range of biometric data, enabling the creation of a comprehensive stress profile. Artificial intelligence algorithms analyze these multimodal data streams in real-time, offering predictive insights and personalized, actionable recommendations regarding stress levels. For instance, activity readings detect acute stress responses, while heart rate variability patterns provide early indicators of chronic stress. These physiological metrics are contextualized with data on health-related social needs—such as access to care, financial strain, and work-life demands—to deliver a holistic, individualized view of maternal well-being. The broader societal and commercial impact includes addressing the rising rates of cardiometabolic and mental health-related maternal morbidity and mortality. By adopting this technology, users gain earlier awareness of their health risks, personalized strategies to manage stress, and better connections to resources, empowering them to improve their overall health outcomes during pregnancy and postpartum.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Award Number: 2524886
Principal Investigator: Yi Wang
Funds Obligated: $50,000
State: SC