Pathological gait clustering in post-stroke patients using motion capture data

BACKGROUND: Analyzing the complex gait patterns of post-stroke patients with lower limb paralysis is essential for rehabilitation. RESEARCH QUESTION: Is it feasible to use the full joint-level kinematic features extracted from the motion capture data of patients directly to identify the optimal gait types that ensure high classification performance? METHODS: In this study, kinematic features were extracted from 111 gait cycle data on joint angles, and angular velocities of 36 post-stroke patients were collected eight times over six months using a motion capture system. Simultaneous clustering and classification were applied to determine the optimal gait types for reliable classification performance. RESULTS: score of 1.0000, respectively. SIGNIFICANCE: There is no distinct clinical classification of post-stroke hemiplegic gaits. However, in contrast to previous studies, more optimal gait types with a high classification performance fully utilizing the kinematic features were identified in this study.