Abstract
Stroke is a major cause of motor disability, degrading walking and quality of life. Wearable gait analysis with magneto-inertial measurement units (MIMUs) can quantify post-stroke impairments. We used machine learning to identify discriminative gait features in stroke, coupling supervised feature selection with unsupervised clustering to improve interpretability and generalizability. Eighty-five stroke patients and 97 healthy controls completed 10-Meter Walk Tests while wearing five MIMUs. Feature selection spanned spatiotemporal, symmetry, stability, and smoothness metrics. K-nearest neighbors (KNN), support vector machines (SVM), and decision trees (TREE) were trained, validated, and tested iteratively across data splits; clustering then verified discriminative ability. Sequential backward feature selection retained nine features, yielding accuracies (healthy vs. patient) of 94.1% (KNN), 96.7% (SVM), and 89.1% (TREE). SVM generalized best. Unsupervised k-medoids with cosine distance confirmed discrimination, reaching 90% accuracy with only three features: stride speed, stance-phase coefficient of variation, and medio-lateral harmonic ratio. Results indicate that gait variability, trunk smoothness, and upper-body stability robustly characterize post-stroke dysfunctions. Notably, head-movement smoothness emerged as a novel, discriminative feature. This integrated framework shows how wearable sensors plus machine learning can support clinical gait analysis and rehabilitation planning. Future work should enable real-time deployment and broaden datasets to cover more clinical scenarios.