Abstract

Understanding the mechanisms behind human balance has been a subject of interest as various postural instabilities have been linked to neuromuscular diseases (e.g., Parkinson's, multiple sclerosis, and concussion). This paper presents a method to characterize an individual's postural stability and estimate of their neuromuscular feedback control parameters. The method uses a generated topological mapping between a subject's experimental data and a dataset consisting of time-series realizations generated using an inverted pendulum mathematical model of upright balance. The performance of the method is quantified using a set of validation time-series realizations with known stability and neuromuscular control parameters. The method was found to have an overall sensitivity of 85.1% and a specificity of 91.9%. Furthermore, the method was most accurate when identifying limit cycle oscillations (LCOs) with a sensitivity of 91.1% and a specificity of 97.6%. Such a method has the capability of classifying an individual's stability and revealing possible neuromuscular impairment related to balance control, ultimately providing useful information to clinicians for diagnostic and rehabilitation purposes.

Issue Section:
Research Papers
Topics:
Stability, Time series, Nonlinear dynamics, Feedback, Equilibrium (Physics), Entropy

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