Hierarchical Gaussian processes for characterizing gait variability in multiple sclerosis

Reduction in mobility due to gait impairment is a critical consequence of diseases affecting the neuromusculoskeletal system, making detecting anomalies in a person’s gait a key area of interest. This challenge is compounded by within-subject and between-subject variability, further emphasized in individuals with multiple sclerosis (MS), where gait patterns exhibit significant heterogeneity. This study introduces a novel perspective on modeling kinematic gait patterns, recognizing the inherent hierarchical structure of the data, which is gathered from contralateral limbs, individuals, and groups of individuals comprising a population, using wearable sensors. Rather than summarizing features, this approach models the entire gait cycle functionally, including its variation. A Hierarchical Variational Sparse Heteroscedastic Gaussian Process was used to model the shank angular velocity across 28 MS and 28 healthy individuals. The utility of this methodology was underscored by its granular analysis capabilities. This facilitated a range of quantifiable comparisons, spanning from group-level assessments to patient-specific analyses, addressing the complexity of pathological gait patterns and offering a robust methodology for kinematic pattern characterization for large datasets. The group-level analysis highlighted notable differences during the swing phase and towards the end of the stance phase, aligning with previously established literature findings. Moreover, the study identified the heteroscedastic gait pattern variability as a distinguishing feature of MS gait. Additionally, a novel approach for lower limb gait asymmetry quantification has been proposed. The use of probabilistic hierarchical modeling facilitated a better understanding of the impaired gait pattern, while also expressing potential for extrapolation to other pathological conditions affecting gait.