Evaluating Stroke-Related Motor Impairment and Recovery Using Macroscopic and Microscopic Features of HD-sEMG

Stroke-induced motor impairment necessitates objective and quantitative assessment tools for rehabilitation planning. In this study, a gesture-specific framework based on high-density surface electromyography (HD-sEMG) was developed to characterize neuromuscular dysfunction using eight macroscopic features and two microscopic motor unit decomposition features. HD-sEMG recordings were collected from stroke patients (n = 11; affected and unaffected sides) and healthy controls (n = 8; dominant side) during seven standardized hand gestures. Feature-level comparisons revealed hierarchical abnormalities, with the affected side showing significantly reduced activation/coordination relative to healthy controls, while the unaffected side exhibited intermediate deviations. For each gesture, dedicated K-nearest neighbors (KNN) models were constructed for clinical validation. For Brunnstrom stage classification, wrist extension yielded the best performance, achieving 92.08% accuracy and effectively discriminating severe (Stage 4), moderate (Stage 5), and mild (Stage 6) impairment as well as healthy controls. For fine motor recovery prediction, the thumb-index-middle finger pinch provided the optimal regression performance, predicting Upper Extremity Fugl-Meyer Assessment (UE-FMA) scores with R = 0.86 and RMSE = 3.24. These results indicate that gesture selection should be aligned with the clinical endpoint: wrist extension is most informative for gross recovery staging, whereas pinch gestures better capture fine motor control. Overall, the proposed HD-sEMG framework provides an objective approach for monitoring post-stroke recovery and supporting personalized rehabilitation assessment.