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Design and Analysis for Fall Detection System Simplification

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Published on: April 6, 2020

Posture analysis in predicting fall-related injuries during French Navy Special Forces selection course using machine

Charles Verdonk^1,2,3, A M Duffaud⁴, A Longin⁵

¹French Armed Forces Biomedical Research Institute, Brétigny-sur-Orge, France verdonk.charles@gmail.com.

BMJ Military Health

|December 21, 2023

Summary

Assessing soldier posture using static posturography and artificial neural networks can help predict fall-related injuries during military training. This approach may enable personalized injury prevention programs for military personnel.

Keywords:

general medicine (see internal medicine)preventive medicine sports medicine

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Area of Science:

Military Medicine
Sports Science
Biomechanical Engineering

Background:

Fall-related injuries are a primary reason for failure in French Navy Special Forces selection.
Predicting individual risk for fall-related injuries is crucial for selection course success.

Purpose of the Study:

To investigate if posture analysis can predict fall-related injuries in military personnel.
To develop a machine learning model for fall-risk assessment.

Main Methods:

Static posturography was used to record postural signals of 99 male soldiers.
An artificial neural network model was developed using machine learning.
The model predicted fall-related injuries leading to course termination.

Main Results:

The artificial neural network model achieved 69.9% accuracy in predicting fall-related injuries.
The model demonstrated an area under the curve of 0.731.
Sensitivity was 56.8% and specificity was 77.7% for injury prediction.

Conclusions:

Posture probing via static posturography and machine learning can inform fall-risk assessment in military training.
This methodology could lead to personalized injury prevention strategies for military populations.
Further validation with larger sample sizes is recommended.