Abstract & Details
Description
Award ID: 1949908
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project introduces a paradigm shift from reaction-based methods to a proactive-based approach to predict the onset of exertional heat illness, which results when the human body fails to maintain core temperature within a narrow range (33.2-38.2). Vulnerable populations include agricultural workers, construction workers, and military personnel, as well as youth athletes. The proposed project will develop a novel low-cost wearable hydration sensor, coupled with predictive analytics, to monitor changes in a youth athletes hydration state. This will mitigate youth injuries, reduce medical costs, and minimize the risk of long-term health conditions. This Small Business Innovation Research (SBIR) Phase I project will develop an integrated hardware-software system to address human hydration in real time. This project will integrate a wearable sensor with cloud-based analytics and prediction. The project objectives are to develop core sensing and algorithm modules, including: 1) exploring signal processing methodologies for signal extraction; 2) development of a framework for learning and predicting temporal patterns in hydration data; 3) experimental determination of typical operating conditions of a wearable sensor. 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 introduces a paradigm shift from reaction-based methods to a proactive-based approach to predict the onset of exertional heat illness, which results when the human body fails to maintain core temperature within a narrow range (33.2-38.2). Vulnerable populations include agricultural workers, construction workers, and military personnel, as well as youth athletes. The proposed project will develop a novel low-cost wearable hydration sensor, coupled with predictive analytics, to monitor changes in a youth athletes hydration state. This will mitigate youth injuries, reduce medical costs, and minimize the risk of long-term health conditions. This Small Business Innovation Research (SBIR) Phase I project will develop an integrated hardware-software system to address human hydration in real time. This project will integrate a wearable sensor with cloud-based analytics and prediction. The project objectives are to develop core sensing and algorithm modules, including: 1) exploring signal processing methodologies for signal extraction; 2) development of a framework for learning and predicting temporal patterns in hydration data; 3) experimental determination of typical operating conditions of a wearable sensor. 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 | 02/01/20 → 11/30/21 |
Lead and Sub-Awardee Organization(s)
Funding
- SBIR Phase I: $222,449.00
Active Fiscal Year
- FY2022
Start Fiscal Year
- FY2020
TIP Programs
- SBIR Phase I
Small Business
- Yes
Key Technology Areas
- Advanced Computing and Semiconductors
- (confidence score: 99%)
Technology Foci
- Advanced Computing and Semiconductors (Broad)
- (confidence score: 100%)
Congressional District at Award
- District n. 02 of North Carolina
Current Congressional District
- District n. 02 of North Carolina
United States
- North Carolina
Core Based Statistical Area (CBSA)
- Raleigh-Cary, NC
County
- County: Wake, NC
Fingerprint
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint. Learn more about Elsevier's Fingerprint Engine here: https://beta.elsevier.com/products/elsevier-fingerprint-engine