Abstract & Details
Description
Award ID: 2026152
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to develop fetal heart rate monitoring technology as part of the wearable fitness technology market, empowering women and clinicians to manage maternal health during pregnancy. The proposed system can disrupt the traditional fetal monitoring methods as the only wireless solution enabling real-time, continuous monitoring when a pregnant woman is exercising. This technology will potentially improve the quality of health and wellness of women and their babies during pregnancy, having the potential to offer new evidence-based guidance. This Small Business Innovation Research (SBIR) Phase I project addresses the technical challenge of developing a wearable fetal monitoring device for pregnant women, specifically tailored for use during exercise. The project will use dry electrodes designed to minimize motion artifacts and improve the quality of the recorded signal, embedded in an abdominal compression garment for ease of use and comfort. The project will also develop machine learning algorithms to separate the confounded maternal and fetal electrocardiogram (ECG) signals and movement artifacts. The project objectives include: (1) optimize spatial density of sensors for accurate measurements; (2) identify the best sensor-incorporating smart clothing design based on functionality; (3) develop and refine signal processing algorithms for accurate filtering and data analysis; and (4)validate the performance of the resulting prototype for accurate fetal monitoring during exercise. 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.
NSF Program Director: Alastair Monk
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to develop fetal heart rate monitoring technology as part of the wearable fitness technology market, empowering women and clinicians to manage maternal health during pregnancy. The proposed system can disrupt the traditional fetal monitoring methods as the only wireless solution enabling real-time, continuous monitoring when a pregnant woman is exercising. This technology will potentially improve the quality of health and wellness of women and their babies during pregnancy, having the potential to offer new evidence-based guidance. This Small Business Innovation Research (SBIR) Phase I project addresses the technical challenge of developing a wearable fetal monitoring device for pregnant women, specifically tailored for use during exercise. The project will use dry electrodes designed to minimize motion artifacts and improve the quality of the recorded signal, embedded in an abdominal compression garment for ease of use and comfort. The project will also develop machine learning algorithms to separate the confounded maternal and fetal electrocardiogram (ECG) signals and movement artifacts. The project objectives include: (1) optimize spatial density of sensors for accurate measurements; (2) identify the best sensor-incorporating smart clothing design based on functionality; (3) develop and refine signal processing algorithms for accurate filtering and data analysis; and (4)validate the performance of the resulting prototype for accurate fetal monitoring during exercise. 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.
NSF Program Director: Alastair Monk
| Status | Closed |
|---|---|
| Effective start/end date | 09/01/20 → 02/28/22 |
Lead and Sub-Awardee Organization(s)
Funding
- SBIR Phase I: $255,762.00
Active Fiscal Year
- FY2022
Start Fiscal Year
- FY2020
TIP Programs
- SBIR Phase I
Small Business
- Yes
Key Technology Areas
- Artificial Intelligence
- (confidence score: 99%)
- Biotechnology
- (confidence score: 98%)
Technology Foci
- Medical Technology
- (confidence score: 100%)
- Artificial Intelligence (Broad)
- (confidence score: 100%)
Congressional District at Award
- District n. 04 of Minnesota
Current Congressional District
- District n. 04 of Minnesota
United States
- Minnesota
Core Based Statistical Area (CBSA)
- Minneapolis-St. Paul-Bloomington, MN-WI
County
- County: Washington, MN
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