Computational Intelligence Driven Motor Function Assessment in Post-Stroke Patients

This paper offers an investigation into leveraging computational intelligence (CI) for the assessment of stroke-induced motor weakness in post-stroke survivors, serving as an indicator of neurological function. The proposed methodology deploys deep learning algorithms to analyze video recordings obtained during the post-stroke hospitalization phase. The model effectively categorizes the degree of stroke-induced weak-ness in the patient's left arm across two and three distinct classes aligned with the National Institutes of Health Stroke Scale. This study was motivated by the limitations of existing monitoring technologies, such as using pressure-sensing mattresses (low resolution and low accuracy). Our long-term strategy is to deploy several means for monitoring the patients' motor function. This study demonstrated a binary classification model using video data collected from a cohort of 23 post-stroke patients in a clinical setting for 48 hours. Employing a 3-fold cross-validation methodology, the developed model showcases an accuracy rate of 92.10
$\pm$
4.08% for the binary classification, distinguishing between mild and severe stroke-induced weakness in the left arm. In the case of three classes, the model achieves an accuracy of 89
$\pm$
4.95%.