The purpose of this study is to investigate the immediate effects of the interventions for core-stability on gait performance of post-stroke patients by using an artificial intelligence (AI)-based motion analysis system, which has been the focus of attention in recent years.
Fifteen stroke patients (mean age 66.5 ± 14.8 years, 11 males and 4 females, the average number of days since onset 108.0 ± 49.7 days) were enrolled in this study. All participants were hospitalized at our hospital, provided informed consent, and were able to gait independently. This study was approved by the Ethical Review Committee of Higashinaebo Hospital. Gait movements were performed three times each pre and post trunk intervention, and two video cameras were used to capture images from two directions. The video cameras were set in the same position during the gait movement to minimize the joint angle errors. The intervention consisted of bilateral trunk muscle stretching and therapeutic intervention of the core-stability for a total of 30 minutes. Using a motion analysis system (HUS Motion Analyzer) based on the posture estimation AI, OpenPose, the average trunk angles and joint angles of the paralyzed leg during two gait cycles were calculated and compared pre- and post-intervention. For statistical analysis, SPSS 26.0 was used. After testing for normality, pre- and post-intervention differences were compared using paired t-test or Wilcoxon's signed rank test, and correlations between each parameter were also calculated. The significance level set to be 0.05.
In the terminal stance phase, the maximum hip extension angle on the paralyzed side significantly increased from 9.4 ± 5.5° before the intervention to 11.5 ± 4.7° after the intervention (p0.01). No significant changes were observed in the angle of trunk forward tilt and in the knee and ankle joints on the paralyzed side. In the comfortable 10-meter gait, both the time and the number of steps significantly decreased after the intervention (p0.05). In addition, a moderate, significant correlation was found between the Trunk Impairment Scale (TIS) and the change in 10-meter walk time (r = 0.644), as well as between the TIS and the change in hip extension angle on the paralyzed side (r = 0.564).
Interventions for core-stability immediately improved gait parameters. Long-term training effects need to be investigated in the future.
This study suggested interventions for core-stability in post-stroke patients was attributed to improve performances in terminal stance of gait. Also, AI-based motion analysis proved to be a useful clinical tool, providing a convenient and efficient way to detect movement changes.
AI
Trunk
