With musculoskeletal injuries accounting for the overwhelming majority of injuries in the military and are often preceded by physical fatigue, scientists at the Johns Hopkins Applied Physics Laboratory, in Laurel, are working on a system to monitor physical fatigue in near-real time. 

Such a capability could prevent countless MSK injuries, and fits into a larger portfolio of work at the APL that is focused on predictive health and human performance modeling.

“We see a high incidence of MSK injuries in the Department of Defense year over year, but physical fatigue, which is a common precursor to those injuries, often goes underreported,” said David Drewry, III, who manages a research portfolio dedicated to Army operational readiness at APL. “In part, that’s unavoidable, because it’s in the nature of military culture to push through the pain and keep working. So there is a real need for objective, quantitative methods to assess whether someone is getting fatigued to the point that they’ve become susceptible to an injury.”

APL biomechanical engineer Mike Vignos is leading an effort, which is currently in its second year, to create machine learning algorithms that use wearable sensor data to reliably identify and quantify the severity of physical fatigue. The long-term goal is to use this information to predict the risk of MSK injury in near-real time and identify those at high risk for injury before it occurs.