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 F-1 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.

Automated summary

This study analyzed the gait patterns of post-stroke patients with lower limb paralysis and extracted kinematic features for clustering and classification. It identified optimal gait types that ensure high classification performance, which is an improvement compared to previous studies that did not fully utilize the kinematic features.