Researchers at the University of Tennessee Chattanooga have developed a platform that measures an athlete’s risk of injury using the Internet of Things (IoT).
The new system could allow athletes at every level, from superstar to hopeful, to create a personal injury risk profile, and manage it from their own smartphones.
Professional athletes live with the knowledge that a serious injury could occur at any moment. Beyond the physical repercussions, these apparent twists of fate can damage successful careers, affect team members or clubs, and have a lasting impact economically and psychologically.
Part of the solution to the ever-present threat of injuries lies in no longer treating them as bad luck, claim researchers. Instead, athletes and their trainers or managers can use new technology to help predict when they might occur.
Using the IoT, researchers at the University of Tennessee Chattanooga have developed a framework to predict and help reduce the risk of injury.
Their research is set out in Mitigating sports injury risks using Internet of Things and analytic approaches, a paper published in the journal Risk Analysis. It explains how screening procedures can help predict the likelihood of an injury using wireless devices and cloud analytics.
Creating a dashboard for injury risk
Sports injury management, even at a professional level, will always rely on some form of subjective assessment. That might come from the athlete in question, who’s determined to run or play in the next game, despite the pain. Or it might come from a doctor who has to interpret that information and make a split-second decision, while facing commercial or personal pressures.
However, the University of Tennessee Chattanooga researchers have done their best to remove this element from the screening process – or at least to provide as much objective data as possible to minimise the risk.
This greater objectivity is added by combining the athlete’s previous injury history with the results of a number of standardised screening tests. The result is a real-time dashboard providing details of each individual athlete’s status.
Data, screening, and predictive analytics
The research project was developed in real-world conditions with a team of American footballers.
A month before the players got together for preseason training, information on their previous injuries was collected using a Sport Fitness Index (SFI) survey. Each player then took a Unilateral Forefoot Squat (UFS) test, which assessed their ability to synchronise muscle responses in their legs while holding an upright position.
The researchers used accelerometers built into their smartphones to measure the results. The collected data was then integrated with the athletes’ self-reports of previous injuries and with longitudinal tracking of exposure to game conditions.
In their analysis of the data, the researchers found the ‘red zone’: athletes who played at least eight games were over three times more likely to suffer an injury than those who played fewer than eight games. Of those athletes who exhibited at least one risk factor, 42 percent then sustained an injury.
“Assigning all athletes to a single type of training program, without consideration of an individual’s unique risk profile, may fail to produce a substantial decrease in injury likelihood,” wrote Gary Wilkerson, lead author of the study.
“The results also provide a useful estimation of the odds of injury occurrence for each athlete during the subsequent season.”
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Moving forward, Wilkerson and his team predict that the prevalence of smartphones and other IoT devices will help to make these and similar screening tests more accessible to athletes at all levels.
Anybody participating in sport could then put all of their data together to identify their own personalised injury risk. A truly smart solution to a painful – and often costly – problem.
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