  This article contributes to the search for a notion of postural style,
focusing on the issue of classifying subjects in terms of how they maintain
posture. Longer term, the hope is to make it possible to determine on a case by
case basis which sensorial information is prevalent in postural control, and to
improve/adapt protocols for functional rehabilitation among those who show
deficits in maintaining posture, typically seniors. Here, we specifically
tackle the statistical problem of classifying subjects sampled from a two-class
population. Each subject (enrolled in a cohort of 54 participants) undergoes
four experimental protocols which are designed to evaluate potential deficits
in maintaining posture. These protocols result in four complex trajectories,
from which we can extract four small-dimensional summary measures. Because
undergoing several protocols can be unpleasant, and sometimes painful, we try
to limit the number of protocols needed for the classification. Therefore, we
first rank the protocols by decreasing order of relevance, then we derive four
plug-in classifiers which involve the best (i.e., more informative), the two
best, the three best and all four protocols. This two-step procedure relies on
the cutting-edge methodologies of targeted maximum likelihood learning (a
methodology for robust and efficient inference) and super-learning (a machine
learning procedure for aggregating various estimation procedures into a single
better estimation procedure). A simulation study is carried out. The
performances of the procedure applied to the real data set (and evaluated by
the leave-one-out rule) go as high as an 87% rate of correct classification (47
out of 54 subjects correctly classified), using only the best protocol.
