A trans-institutional team of Vanderbilt engineering, data science and clinical researchers has developed a novel approach for monitoring bone stress in recreational and professional athletes, with the goal of anticipating and preventing injury.
Using machine learning and biomechanical modeling techniques, the researchers built multisensory algorithms that combine data from lightweight, low-profile wearable sensors in shoes to estimate forces on the tibia, or shin bone—a common place for runners’ stress fractures.
This highlights why it is so important for us to develop more accurate techniques to monitor bone load and design next-generation wearables.”
The goal of this tech is to better understand overuse injury risk factors and then prompt runners to take rest days or modify training before an injury occurs.
“The machine learning algorithm leverages the Least Absolute Shrinkage and Selection Operator regression, using a small group of sensors to generate highly accurate bone load estimates, with average errors of less than three percent, while simultaneously identifying the most valuable sensor inputs,” said Peter Volgyesi, a research scientist at the Vanderbilt Institute for Software Integrated Systems.
This is a highly practical application of machine learning, markedly demonstrating the power of interdisciplinary collaboration with real-life broader impact.”