  Falling in Parkinsonian syndromes (PS) is associated with postural
instability and consists a common cause of disability among PS patients.
Current posturographic practices record the body's center-of-pressure
displacement (statokinesigram) while the patient stands on a force platform.
Statokinesigrams, after appropriate signal processing, can offer numerous
posturographic features, which however challenges the efforts for valid
statistics via standard univariate approaches. In this work, we present the
ts-AUC, a non-parametric multivariate two-sample test, which we employ to
analyze statokinesigram differences among PS patients that are fallers (PSf)
and non-fallers (PSNF). We included 123 PS patients who were classified into
PSF or PSNF based on clinical assessment and underwent simple Romberg Test
(eyes open/eyes closed). We analyzed posturographic features using both
multiple testing with p-value adjustment and the ts-AUC. While the ts-AUC
showed significant difference between groups (p-value = 0.01), multiple testing
did not show any such difference. Interestingly, significant difference between
the two groups was found only using the open-eyes protocol. PSF showed
significantly increased antero-posterior movements as well as increased
posturographic area, compared to PSNF. Our study demonstrates the superiority
of the ts-AUC test compared to standard statistical tools in distinguishing PSF
and PSNF in the multidimensional feature space. This result highlights more
generally the fact that machine learning-based statistical tests can be seen as
a natural extension of classical statistical approaches and should be
considered, especially when dealing with multifactorial assessments.
