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Updated: Aug 6, 2026
Asymmetric Walkway: A Novel Behavioral Assay for Studying Asymmetric Locomotion
Published on: January 15, 2016
Multi-Site Identification and Generalization of Clusters of Walking Behaviors in Individuals With Chronic Stroke and
Natalia Sánchez1,2,3, Nicolas Schweighofer3,4,5, Sara J Mulroy3,6
1Department of Physical Therapy, Chapman University, Irvine, CA, USA.
Background:
Walking patterns in stroke survivors are highly heterogeneous, which poses a challenge in systematizing treatment prescriptions for walking rehabilitation interventions.
Objectives:
We used bilateral spatiotemporal and force data during walking to create a multi-site research sample to: (1) identify clusters of walking behaviors in people post-stroke and neurotypical controls and (2) determine the generalizability of these walking clusters across different research sites. We hypothesized that participants post-stroke will have different walking impairments resulting in different clusters of walking behaviors, which are also different from control participants.
Methods:
We gathered data from 81 post-stroke participants across 4 research sites and collected data from 31 control participants. Using sparse K-means clustering, we identified walking clusters based on 17 spatiotemporal and force variables. We analyzed the biomechanical features within each cluster to characterize cluster-specific walking behaviors. We also assessed the generalizability of the clusters using a leave-one-out approach.
Results:
We identified 4 stroke clusters: a fast and asymmetric cluster, a moderate speed and asymmetric cluster, a slow cluster with frontal plane force asymmetries, and a slow and symmetric cluster. We also identified a moderate speed and symmetric gait cluster composed of controls and participants post-stroke. The moderate speed and asymmetric stroke cluster did not generalize across sites.
Conclusions:
Although post-stroke walking patterns are heterogenous, these patterns can be systematically classified into distinct clusters based on spatiotemporal and force data. Future interventions could target the key features that characterize each cluster to increase the efficacy of interventions to improve mobility in people post-stroke.
