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Introduction Athletes and military personnel are both at risk of disabling injuries due to extreme physical activity. A method to predict which individuals might be more susceptible to injury would be valuable, especially in the military where basic recruits may be discharged from service due to injury. We postulate that certain body characteristics may be used to predict risk of injury with physical activity. Methods US Army basic training recruits between the ages of 17 and 21 (N = 17,680, 28% female) were scanned for uniform fitting using the 3D body imaging scanner, Human Solutions of North America at Fort Jackson, SC. From the 3D body imaging scans, a database consisting of 161 anthropometric measurements per basic training recruit was used to predict the probability of discharge from the US Army due to injury. Predictions were made using logistic regression, random forest, and artificial neural network (ANN) models. Model comparison was done using the area under the curve (AUC) of a ROC curve.

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BackgroundPosture, obesity, and body shape are well‐established predictors of injury and athletic performance. However, due to the manual burden in collecting numerous anthropometric measures, to date, no large database and systematic data analysis determining body shape effects on performance and injury have gone beyond using standard measurements like BMI, waist circumference and hip circumference.ObjectiveTo use machine learning based Artificial Neural Network (ANN) and decision tree analysis on a large nationally representative database of automatically captured body anthropometrics to predict basic training‐related injury.MethodsOver N=20,896soldiers (28% female) recruited for US Army basic training at Fort Jackson, SC were scanned for uniform fitting using the Human Solutions Kinect based 3D imaging technology. Each subject image consisted of 161 body shape measurements. After removing subjects with missing measurements, the remaining subjects were split into injured (91 recruits) and non‐injured (13,296 recruits) groups. 75% of each group was placed into a training group and the other 25% into a testing group. An ANN and Decision Tree model evaluated using the area under the curve(AUC) was developed to predict severe physical injury occurrence during 10 weeks of US Army basic training. Body length measurement was compared against height to evaluate whether certain body proportions were more at risk for injury.ResultsThe AUC for the ANN was 0.79 and the AUC for the Decision Tree model was 0.70. It was determined that body proportions of shorter legs and longer torso more than doubled the risk of injury during US Army basic trainingConclusionsMachine learning models that leverage data sources like those from the newly emerging 3D body image scanners may be used to predict severe injury during vigorous physical activity. These results can be used to develop personalized prevention strategies reducing injury and retaining individuals within their activity protocols.Support or Funding InformationNoneThis abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.