Phase-Specific Biomechanical Reorganization After Robotic Rehabilitation in Patients with Stroke: A Sensor-Derived...

Stroke-related gait impairments are frequently associated with deficits in trunk control, movement coordination, and dynamic stability. Although robotic-assisted gait rehabilitation has shown promising clinical benefits, phase-specific biomechanical adaptations following rehabilitation remain incompletely understood. This study investigated phase-specific biomechanical adaptations following robotic-assisted gait rehabilitation in individuals with stroke using sensor-derived waveform analysis. Rehabilitation was performed three times per week over approximately 5-6 weeks using treadmill-based robotic gait training under dynamic body-weight support conditions. Pre- and post-intervention kinematic data were collected using a sensor-based motion analysis system. Joint kinematics, trunk motion, and center of gravity (COG) displacement were analyzed across the normalized gait cycle using waveform-based effect size analysis, statistical parametric mapping, principal component analysis, and k-means clustering to explore inter-individual adaptation patterns. Thirteen post-stroke hemiplegia patients (10 males; age = 63.9 ± 13.8 years), including six subacute and seven chronic stroke survivors, completed 16 rehabilitation sessions. The most prominent improvements were observed in trunk lateral flexion, particularly during loading response (d = 0.47, p < 0.01), indicating enhanced frontal plane trunk stability. Trunk flexion-extension showed reduced compensatory motion, whereas hip and knee adaptations were smaller and phase-dependent. COG displacement decreased across the gait cycle, reflecting improved dynamic stability. Step length increased significantly on both hemiplegic (Δ = +5.73 cm, p = 0.024) and intact sides (Δ = +8.83 cm, p = 0.007), while cadence and load symmetry remained unchanged. Clustering analysis revealed heterogeneous adaptation profiles rather than distinct responder groups. Chronic participants demonstrated greater variability within the Principal Component Analysis space compared to subacute participants, suggesting more variable and individualized biomechanical reorganization patterns rather than clearly separable recovery categories. Overall, robotic rehabilitation induced inter-individual biomechanical adaptations, predominantly involving proximal trunk control and stabilization strategies.