Classification in postural style
Antoine Chambaz, Christophe Denis

TL;DR
This study develops a statistical classification method for postural style based on complex trajectory data, aiming to identify key protocols for efficient and less invasive assessment of postural control deficits.
Contribution
The paper introduces a novel classification approach combining targeted maximum likelihood learning and super-learning to select the most relevant protocols for postural assessment.
Findings
Achieved up to 87% classification accuracy using only the most relevant protocol.
Demonstrated the effectiveness of a protocol ranking and selection strategy.
Validated the approach with real and simulated data.
Abstract
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…
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Taxonomy
TopicsWinter Sports Injuries and Performance · Gait Recognition and Analysis · Infrared Thermography in Medicine
