ABSTRACT

Strokes are the third leading cause of disability worldwide. Currently, several types of performance assessment, such as the Fugl–Meyer index, are used to explore the overall difference between cerebral infarction (CI) and cerebral hemorrhage (CH) post-stroke patients. However, these performance assessments ignore subtle differences in the limbs of patients, which could be helpful for rehabilitation training. This study was designed to determine and evaluate the differences between the limbs of CI and CH patients. First, we collected the kinematic data of patients and extracted the spatio-temporal features. Then, we developed four different models to classify the CI and CH patients, in which a linear support vector machine (LSVM) classifier method achieved an 80.1% classification accuracy. Finally, we calculated the decision boundary of the shoulder and ankle marker position features separately based on the LSVM model. From the decision boundary, we determined that the CI patients' shoulder position appeared to be anterior to that of the CH patients, and the CH patients had a wider stance width compared to the CI patients. Such findings can serve as guidance for doctors and help provide professional rehabilitation courses for post-stroke patients.

摘要

中风是全球第三大致残原因。目前，几种类型的绩效评估，例如 Fugl-Meyer 指数，用于探索脑梗塞 (CI) 和脑出血 (CH) 后中风患者之间的总体差异。然而，这些性能评估忽略了患者肢体的细微差异，这可能有助于康复训练。本研究旨在确定和评估 CI 和 CH 患者肢体之间的差异。首先，我们收集了患者的运动学数据并提取了时空特征。然后，我们开发了四种不同的模型来对 CI 和 CH 患者进行分类，其中线性支持向量机 (LSVM) 分类器方法实现了 80.1% 的分类准确率。最后，我们基于LSVM模型分别计算了肩部和脚踝标记位置特征的决策边界。从决策边界，我们确定 CI 患者的肩部位置似乎比 CH 患者的肩位置靠前，并且与 CI 患者相比，CH 患者的站立宽度更宽。这些发现可以作为医生的指导，并有助于为中风后患者提供专业的康复课程。