Clasificación Funcional de la Marcha Hemiparética Basada en el Ángulo de Extensión de la Pierna Afectada y la...

Hemiparetic gait after stroke shows substantial individual variability, and understanding its biomechanical determinants is essential for developing targeted rehabilitation strategies.

The objective of this study was to classify hemiparetic gait into subgroups based on paretic leg extension angle and gastrocnemius muscle activity during stance and to examine differences in walking ability among these subgroups.

This study used a cross-sectional observational design with hierarchical cluster analysis.

Data were collected in a hospital rehabilitation department.

Eighty-three individuals with chronic stroke who could walk independently participated.

Leg extension angle and gastrocnemius muscle activity during stance were assessed using a video-based markerless motion analysis system and surface electromyography.

Spatiotemporal gait parameters, gait asymmetry, peak knee flexion angle during swing, and clinical assessments of motor paresis and spasticity were compared among the identified clusters.

Hierarchical cluster analysis identified 4 gait patterns based on paretic leg extension angle and gastrocnemius activity during stance: Cluster 1 (large leg extension angle with moderate gastrocnemius activity) showed walking speed comparable to Cluster 3 but with reduced knee flexion during swing; Cluster 2 (moderate leg extension angle with moderate gastrocnemius activity) demonstrated intermediate walking speed that was slower than that of Cluster 1; Cluster 3 (large leg extension angle with the greatest gastrocnemius activity) exhibited faster walking speed and the greatest peak knee flexion during swing; and Cluster 4 (small leg extension angle with reduced gastrocnemius activity) showed the slowest walking speed, significant gait asymmetry, and severe motor paresis.

Hemiparetic gait was classified into 4 patterns based on leg extension angle and gastrocnemius activity, and these patterns were associated with differences in walking ability.

This functional classification framework may help clinicians identify key biomechanical targets and support the design of individualized rehabilitation strategies after stroke.