Posture analysis in predicting fall-related injuries during French Navy Special Forces selection course using machine learning: a proof-of-concept study

Abstract

IntroductionInjuries induced by falls represent the main cause of failure in the French Navy Special Forces selection course. In the present study, we made the assumption that probing the posture might contribute to predicting the risk of fall-related injury at the individual level.MethodsBefore the start of the selection course, the postural signals of 99 male soldiers were recorded using static posturography while they were instructed to maintain balance with their eyes closed. The event to be predicted was a fall-related injury during the selection course that resulted in the definitive termination of participation. Following a machine learning methodology, we designed an artificial neural network model to predict the risk of fall-related injury from the descriptors of postural signal.ResultsThe neural network model successfully predicted with 69.9% accuracy (95% CI 69.3–70.5) the occurrence of a fall-related injury event during the selection course from the selected descriptors of the posture. The area under the curve value was 0.731 (95% CI 0.725–0.738), the sensitivity was 56.8% (95% CI 55.2–58.4) and the specificity was 77.7% (95% CI 76.8–0.78.6).ConclusionIf confirmed with a larger sample, these findings suggest that probing the posture using static posturography and machine learning-based analysis might contribute to inform risk assessment of fall-related injury during military training, and could ultimately lead to the development of novel programmes for personalised injury prevention in military population.