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: In the given dataset, six optimal gait groups were identified, and the clustering and classification performances were denoted by a silhouette coefficient of 0.1447 and F₁ 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.