On Gait Consistency Quantification Through ARX Residual Modeling and Kernel Two-Sample Testing

The quantification of the way an individual walks is key to the understanding of diseases affecting the neuromuscular system. More specifically, to improve diagnostics and treatment plans, there is a continuous interest in quantifying gait consistency, allowing clinicians to distinguish natural variability of the gait patterns from disease progression or treatment effects. To this end, the current article presents a novel objective method for assessing the consistency of an individual's gait, consisting of two major components.

Firstly, inertial sensor accelerometer data from both shanks and the lower back is used to fit an AutoRegressive with eXogenous input model. The model residuals are then used as a key feature for gait consistency monitoring. Secondly, the non-parametric maximum mean discrepancy hypothesis test is introduced to measure differences in the distributions of the residuals as a measure of gait consistency. As a paradigmatic case, gait consistency was evaluated both in a single walking test and between tests at different time points in healthy individuals and those affected by multiple sclerosis (MS).

It was found that MS patients experienced difficulties maintaining a consistent gait, even when the retest was performed one-hour apart and all external factors were controlled. When the retest was performed one-week apart, both healthy and MS individuals displayed inconsistent gait patterns.

Gait consistency has been successfully quantified for both healthy and MS individuals.

This newly proposed approach revealed the detrimental effects of varying assessment conditions on gait pattern consistency, indicating potential masking effects at follow-up assessments.