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.