Distinguishing features of postural instability in Parkinson’s disease identified through wireless insole plantar pressure monitoring during daily living activities

Abstract

Postural instability (PI) is one of the most disabling motor signs of Parkinson’s disease (PD) and often underlies an increased likelihood of falling and loss of independence. Current clinical assessments of PD-related PI are based on a retropulsion test, which introduces human error and only evaluates reactive balance. There is an unmet need for objective, multi-dimensional assessments of PI that directly reflect activities of daily living in which individuals may experience PI. In this study, we trained machine learning models on insole plantar pressure data from 111 participants (44 with PD and 67 controls) as they performed static and active postural tasks of daily living. Models accurately classified PD from young controls (area under the curve (AUC) 0.99 +/- 0.00), PD from age-matched controls (AUC 0.99 +/- 0.01), and PD fallers from PD non-fallers (AUC 0.91 +/- 0.08). Utilizing features from both static and active postural tasks significantly improved classification performances, and all tasks were useful for separating PD from controls; however, tasks with higher postural threat were preferred for separating PD fallers from PD non-fallers.