Evaluating quiet standing posture of post-stroke patients by classifying cerebral infarction and cerebral hemorrhage patients

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.