Technology Driven Tools for Horse Owners, Trainers and Riders

Project Overview

This project targets the integration of technology, phenotyping, and big data phenotyping for developing workload modeling and defining nutrient requirements for enhanced equine performance. The team is developing analytical models and developing a smartphone app that calculates energy expenditure of the horse and related nutritional and dietary adjustments using a variety of measures much like the Fitbit human applications. This will become a case study for internet of things and cyberbiosecurity. 

Project Team

• Sally Johnson, Middleburg AREC/Animal and Poultry Sciences
• Tait Golightly, Middleburg AREC
• Jay Williams, Human Nutrition, Foods, and Exercise

Key Finding:

Current technologies used for human athletic performance monitoring translate well to the working horse. Use of an inertial sensor on the front forelimb allows for the capture of sufficient information to assess exercise workload. The sensor may be a user-friendly alternative to wearable heart rate monitors.

Key Outcomes:

Testing the device on horses served as an experiential learning opportunity to multiple undergraduate students during the 1-year funding period.  The students gained exposure to large dataset capture, analysis and implementation. 

Next Steps:

In a new partnership with an equine nutrition company, the research team will use the sensors to monitor horse activity for the calculation of energy expenditures.