QUANTICO, Va. – Sparta Science, the industry leader in force plate and machine learning technology to enhance prediction of injury risk and injury mitigation, has partnered with The Marine Corps Training and Education Command (TECOM) at Marine Corps Base Quantico. Leveraging a database of over two million scans, Sparta Science’s Force Plate Machine Learning™ can analyze the movements of a warfighter in a matter of seconds to understand their performance levels and identify areas with an increased risk of musculoskeletal (MSK) injury. By empowering Marines and commanders with individualized data-backed insights, Sparta’s technology will increase readiness and improve fitness.
By incorporating the Sparta Science system into a holistic fitness program, Marines will not only be better equipped to prevent injuries, but they will have clear cut answers to why some injuries happen, as well as guidance on the most efficient way to rehabilitate.
TECOM has introduced Sparta Science technology to Marine Corps Recruiting Depots, Officer Candidate School (OCS), USMC School of Infantry, and Infantry Officer Course.
“Prior to force plate machine learning technology, we did not know the causes of MSK injury attrition,” said Stephen Armes, former Director of Human Performance Division. “Force plate machine learning will be a game changer in reducing attrition across the entire force by providing the first ever physiological evaluation, which will also build physical and mental resiliency.”
Sparta Science Force Plate Machine Learning™ aims to increase readiness by streamlining recruitment productivity, improving entry level training, and reducing attrition. Integrating Sparta Science technology into OCS preparation will allow candidates to access important data about their training weeks before starting OCS.
Sparta Science currently partners with several commands across the U.S. Armed Forces to help increase readiness, as well as achieve and maintain the needed fitness levels for warfighters in a way that is both tailored to the individual and can be deployed at scale.