Data-driven analysis of heterogeneous gait subgroups and ground reaction forces based on integrated center of pressure–center of mass dynamics in poststroke hemiparesis

 This didn't tell me ONE DAMN THING that will get survivors recovered! You're all fired!

Maybe you could get something from these instead, I'm sure your doctor isn't up-to-date on all this.

• ground reaction forces (2 posts to January 2020)
• gait analysis (122 posts to May 2015)
• center of pressure (2 posts to January 2025)
• center of mass (1 post to June 2020)

Data-driven analysis of heterogeneous gait subgroups and ground reaction forces based on integrated center of pressure–center of mass dynamics in poststroke hemiparesis

Kimihiko Mori ,Tatsuya Teramae,Masanori Wakida,Naoto Mano,Yuta Chujo,Takayuki Kuwabara,Meguru Taguchi,Kimitaka Hase,Tomoyuki Noda

Abstract

Introduction

Hemiparetic gait is characterized by abnormal ground reaction forces (GRFs) with impaired control of the center of mass (CoM) relative to the center of pressure (CoP). Although both anteroposterior and mediolateral gait control have been examined separately, how their integrated stance-phase–derived CoP–CoM interactions and local CoP-based features relate to GRF characteristics remains unclear.

Objective

This exploratory study aimed to explore the relationships between CoP–CoM and CoP-based dynamics and GRFs and descriptively identify candidate gait subgroups of individuals with poststroke hemiparesis using a data-driven clustering approach.

Methods

Seventy-eight community-dwelling individuals with poststroke hemiparesis participated in a three-dimensional gait analysis. Stance-phase–derived CoP–CoM parameters of the transverse plane and local CoP-based loading metrics were extracted during the paretic stance phase. Relationships between these gait metrics and GRFs, particularly early braking force, propulsion, and late braking force, were examined using nonparametric correlation analyses. K-means clustering was performed to explore candidate gait subgroups, and inter-cluster differences were exploratorily examined.

Results

Across all participants, several CoP–CoM interaction metrics showed significant correlations with GRFs. In particular, insufficient forward progression of the CoM relative to the CoP during late stance showed a strong correlation with late braking force (rs = 0.79). Clustering suggested the presence of four candidate gait subgroups characterized by differing combinations of anteroposterior and mediolateral CoP–CoM dynamics and local CoP loading features. However, the results of clusters with small sample sizes warrant cautious interpretation.

Conclusions

Integrated stance-phase–derived CoP–CoM dynamics and CoP-based parameters may highlight heterogeneity in hemiparetic gait and may have meaningful associations with GRF characteristics. The candidate gait subgroups should be interpreted as hypothesis-generating and may offer a descriptive framework for understanding diverse gait disorders.