Predicting running injury using kinematic and kinetic parameters generated by an optical motion capture system

• DOI: 10.1007/s42452-019-0695-x
• Publication Date: 2019-06-07T13:02:40Z
• Authors: Sinan Onal; Mitchell Leefers; Bryan Smith; Sohyung Cho
• Keywords: Motion Capture; Boosting

Although runners are at high risk of back and lower extremity injuries, available tools detect only current injury. Here, a model was developed to analyze kinetic and kinematic running gait data collected by an optical motion capture system to predict future injuries based on an individual’s running gait pattern. The two key points, when the joints are most vulnerable because internal forces are the greatest, in the continuous running gait cycle were used to extract average parameter values to create predictive models: the heel strike, and when one leg supports the body weight. Three different prediction models—logistic regression, random forest, and boosting—were built using 10 significant parameters identified in a two-step feature selection approach. All collected metric data were normalized before building the predictive models to avoid outlier values and redundancy. The three models were tested to determine whether they could predict that a participant would incur chronic running injuries in the future based on their current running gait pattern. The logistic regression model had the highest prediction accuracy: the area under the curve was 0.9016 [95% confidence interval (CI) 0.8808–0.9369] for logistic regression, 0.8892 (95% CI 0.8463–0.9152) for the random forest, and 0.8732 (95% CI 0.8401–0.9178) for boosting. Further model development may not only enable clinicians to integrate injury intervention into running programs but also lead to predictive models that recognize patterns associated with neurological disorders, such as Parkinson’s disease, autism, and multiple sclerosis, in which gait and balance deficiencies may be symptoms or even predictors of disease.