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.