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Updated: Jun 15, 2025
Motor Dual-Tasks for Gait Analysis and Evaluation in Post-Stroke Patients
Published on: March 11, 2021
Center of Pressure- and Machine Learning-based Gait Score and Clinical Risk Factors for Predicting Functional Outcome
Eun-Tae Jeon1, Sang-Hun Lee1, Mi-Yeon Eun2
1Department of Neurology, Korea University Ansan Hospital, Korea University College of Medicine, Ansan.
Objectives:
To investigate whether machine learning (ML)-based center of pressure (COP) analysis for gait assessment, when used in conjunction with clinical information, offers additive benefits in predicting functional outcomes in patients with acute ischemic stroke.
Design:
A prospective, single-center cohort study.
Setting:
A tertiary hospital setting.
Participants:
A total of 185 patients with acute ischemic stroke, capable of walking 10 m with or without a gait aid by day 7 postadmission. From these patients, 10,804 pairs of consecutive footfalls were included for analysis.
Interventions:
Not applicable.
Main Outcome Measures:
The dependent variable was a 3-month poor functional outcome, defined as modified Rankin scale score ≥2. For independent variables, 65 clinical variables including demographics, anthropometrics, comorbidities, laboratory data, questionnaires, and drug history were included. Gait function was evaluated using a pressure-sensitive mat. Time-series COP data were parameterized into spatial and temporal variables and analyzed with logistic regression and 2 ML models (light gradient-boosting machine and multilayer perceptron [MLP]). We derived GAIT-AI output scores from the best-performing model analyzed COP data and constructed multivariable logistic regression models using clinical variables and the GAIT scores.
Results:
Among the included patients, 70 (37.8%) experienced unfavorable outcomes. The MLP model demonstrated the highest predictive performance with an area under the receiver operating characteristic curve (AUROC) of 0.799. Multivariable logistic regression identified age, initial National Institutes of Health Stroke Scale, and initial Fall Efficacy Scale-International as associated factors with unfavorable outcomes. The combined multivariable logistic regression incorporating COP-derived output scores improved the AUROC to 0.812.
Conclusions:
Gait function, assessed through COP analysis, serves as a significant predictor of functional outcome in patients with acute ischemic stroke. ML-based COP analysis, when combined with clinical data, enhances the prediction of poor functional outcomes.
