2335352
Project Grant
Overview
Grant Description
Sttr Phase I: DigitFoal: An early labor warning system for safe and successful foal delivery.
The broader/commercial impact of this STTR Phase I project is reflected in the innovation of DigitFoal, a sophisticated early labor warning system tailored for the equine industry.
By leveraging state-of-the-art dual wearable photoplethysmography (PPG) sensors and a pre-trained deep learning algorithm, this system is poised to greatly mitigate the foal mortality due to dystocia.
It is projected to reduce foal deaths by 30%, translating to a substantial economic benefit of over $82.5 million annually.
The broader implications extend beyond economic savings, promising enhancements in scientific understanding and livestock management.
This technology could revolutionize not only equine breeding practices but also be adaptable for monitoring other livestock and wildlife, thereby contributing to broader societal, environmental, and educational advancements.
The intellectual merit of this project is anchored in the integration of innovative PPG sensor technology with a robust echo state network model to monitor and analyze critical physiological signals of horses, which are predictive of foaling stages.
This approach is designed to fill a significant void in current market offerings that largely depend on manual monitoring and are plagued by high rates of inaccuracy and labor intensity.
The research will further refine the predictive capabilities of the technology, facilitating real-time, accurate assessments of foaling risks.
This project is expected to yield significant advancements in non-invasive, real-time animal monitoring systems, contributing invaluable knowledge to the field of precision livestock farming and enhancing the technological landscape of animal health monitoring.
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.
Subawards are planned for this award.
The broader/commercial impact of this STTR Phase I project is reflected in the innovation of DigitFoal, a sophisticated early labor warning system tailored for the equine industry.
By leveraging state-of-the-art dual wearable photoplethysmography (PPG) sensors and a pre-trained deep learning algorithm, this system is poised to greatly mitigate the foal mortality due to dystocia.
It is projected to reduce foal deaths by 30%, translating to a substantial economic benefit of over $82.5 million annually.
The broader implications extend beyond economic savings, promising enhancements in scientific understanding and livestock management.
This technology could revolutionize not only equine breeding practices but also be adaptable for monitoring other livestock and wildlife, thereby contributing to broader societal, environmental, and educational advancements.
The intellectual merit of this project is anchored in the integration of innovative PPG sensor technology with a robust echo state network model to monitor and analyze critical physiological signals of horses, which are predictive of foaling stages.
This approach is designed to fill a significant void in current market offerings that largely depend on manual monitoring and are plagued by high rates of inaccuracy and labor intensity.
The research will further refine the predictive capabilities of the technology, facilitating real-time, accurate assessments of foaling risks.
This project is expected to yield significant advancements in non-invasive, real-time animal monitoring systems, contributing invaluable knowledge to the field of precision livestock farming and enhancing the technological landscape of animal health monitoring.
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.
Subawards are planned for this award.
Awardee
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH (SBIR)/ SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAMS PHASE I", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF23515
Grant Program (CFDA)
Awarding Agency
Place of Performance
Columbia,
Missouri
65203-6614
United States
Geographic Scope
Single Zip Code
Laser Graphictronics was awarded
Project Grant 2335352
worth $275,000
from in October 2024 with work to be completed primarily in Columbia Missouri United States.
The grant
has a duration of 1 year and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
The Project Grant was awarded through grant opportunity NSF Small Business Innovation Research / Small Business Technology Transfer Phase I Programs.
SBIR Details
Research Type
STTR Phase I
Title
STTR Phase I: DigitFoal: An Early Labor Warning System for Safe and Successful Foal Delivery
Abstract
The broader/commercial impact of this STTR Phase I project is reflected in the innovation of DigitFoal, a sophisticated early labor warning system tailored for the equine industry. By leveraging state-of-the-art dual wearable photoplethysmography (PPG) sensors and a pre-trained deep learning algorithm, this system is poised to greatly mitigate the foal mortality due to dystocia. It is projected to reduce foal deaths by 30%, translating to a substantial economic benefit of over $82.5 million annually. The broader implications extend beyond economic savings, promising enhancements in scientific understanding and livestock management. This technology could revolutionize not only equine breeding practices but also be adaptable for monitoring other livestock and wildlife, thereby contributing to broader societal, environmental, and educational advancements.
The intellectual merit of this project is anchored in the integration of innovative PPG sensor technology with a robust echo state network model to monitor and analyze critical physiological signals of horses, which are predictive of foaling stages. This approach is designed to fill a significant void in current market offerings that largely depend on manual monitoring and are plagued by high rates of inaccuracy and labor intensity. The research will further refine the predictive capabilities of the technology, facilitating real-time, accurate assessments of foaling risks. This project is expected to yield significant advancements in non-invasive, real-time animal monitoring systems, contributing invaluable knowledge to the field of precision livestock farming and enhancing the technological landscape of animal health monitoring.
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.
Topic Code
I
Solicitation Number
NSF 23-515
Status
(Complete)
Last Modified 9/10/25
Period of Performance
10/1/24
Start Date
9/30/25
End Date
Funding Split
$275.0K
Federal Obligation
$0.0
Non-Federal Obligation
$275.0K
Total Obligated
Activity Timeline
Transaction History
Modifications to 2335352
Additional Detail
Award ID FAIN
2335352
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
JD4RKHYNVUB1
Awardee CAGE
993M9
Performance District
MO-03
Senators
Joshua Hawley
Eric Schmitt
Eric Schmitt
Modified: 9/10/25