Posture analysis in predicting fall-related injuries during French Navy Special Forces selection course using machine learning: a proof-of-concept study.
BMJ Mil Health
; 2023 Dec 12.
Article
en En
| MEDLINE
| ID: mdl-38124202
ABSTRACT
INTRODUCTION:
Injuries 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.METHODS:
Before 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.RESULTS:
The 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).CONCLUSION:
If 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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
BMJ Mil Health
Año:
2023
Tipo del documento:
Article
País de afiliación:
Francia