A bone stress injury (BSI) means that the bones cannot tolerate repeated mechanical loads, resulting in structural fatigue and local bone pain. A delay in BSI diagnosis can lead to more serious injuries, such as stress fractures that require longer treatment periods.
A team of researchers from 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 commonplace for runners’ stress fractures.
The algorithms have resulted in bone force data that is up to four times more accurate than available wearables, and the study found that traditional wearable metrics based on how hard the foot hits the ground may be no more accurate for monitoring tibial bone load than counting steps with a pedometer, reports Marissa Shapiro in Vanderbilt University.
Bones naturally heal themselves, but if the rate of microdamage from repeated bone loading outpaces the rate of tissue healing, there is an increased risk of a stress fracture that can put a runner out of commission for two to three months.
“Small changes in bone load equate to exponential differences in bone microdamage,” said Emily Matijevich, a graduate student and the director of the Center for Rehabilitation Engineering and Assistive Technology Motion Analysis Lab. “We have found that 10 percent errors in force estimates cause 100 percent errors in damage estimates. Largely over- or under-estimating the bone damage that results from running has severe consequences for athletes trying to understand their injury risk over time. This highlights why it is so important for us to develop more accurate techniques to monitor bone load and design next-generation wearables.”
The ultimate 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.
Peter Volgyesi, a research scientist at the Vanderbilt Institute for Software Integrated Systems commented:
“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,”
This innovation is one of the first examples of a wearable technology that is both practical to wear in daily life and can accurately monitor forces on and microdamage to musculoskeletal tissues. The team has begun applying similar techniques to monitor low back loading and injury risks, designed for people in occupations that require repetitive lifting and bending. These wearables could track the efficacy of post-injury rehab or inform return-to-play or return-to-work decisions, the Vanderbilt University reports said.
The article was published online in the journal Human Movement Science.