Vestibular autorotation test in primary selection of military student pilots using neural networks.
BACKGROUND
Selection of student pilots to the Royal Danish Air Force involves a physical examination including a vestibular test. Usually tests for selection proposes are not well documented.


OBJECTIVE
The result of the vestibular autorotation test (VAT) is correlated to the ability to learn to fly in a military context.


METHODS
A Multi Layer Perception neural network with three layers configured as a Back Propagation Network was tested using data originating from horizontal VAT of 59 student pilot candidates, given the outcome of the pre-jet basic flight check. In the analysis the leave-one-out method was used.


RESULTS
Based on horizontal data only the network correctly classified the student pilot candidates as having been passed or rejected within a verification error margin < 0.1.


CONCLUSIONS
The result indicates that VAT performed at the initial physical examination is a powerful tool for the elimination of unfit student pilot candidates when data are analyzed in neural networks.
Klokker
M
M
Department of Otolaryngology, Head & Neck Surgery, Rigshospitalet, Copenhagen, Denmark.
Brock-Nannestad
L
L
Mikines
P
P
Johnsen
A F
AF
Garhøj
P
P
Vesterhauge
S
S
D000293:Adolescent; D000328:Adult; D000337:Aerospace Medicine; D015982:Bias; D003718:Denmark; D005260:Female; D006801:Humans; D008297:Male; D008403:Mass Screening / Q000379:methods*; D008889:Military Personnel; D016571:Neural Networks, Computer; D010560:Personnel Selection / Q000379:methods*; D010808:Physical Examination / Q000379:methods*; D012027:Reflex, Vestibulo-Ocular; D015203:Reproducibility of Results; D012399:Rotation; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013334:Students / Q000145:classification; D014724:Vestibular Function Tests / Q000379:methods*