Automated EMG-Based Classification of Upper Extremity Motor Impairment Levels in Subacute Stroke

Rehabilitation of upper extremity (UE) impairments after stroke requires regular evaluation, with standard methods typically being time-consuming and relying heavily on manual assessment by therapists. In our study, we propose automating these assessments using electromyography (EMG) as a core indicator of muscle activity, correlating passive and active EMG signals with clinical motor impairment scores. UE motor function in 25 patients was evaluated using the Fugl-Meyer Assessment for UE (FMA-UE), the Modified Ashworth Scale (MAS), and the Brunnstrom Recovery Stages (BRS). EMG data were processed via feature extraction and linear discriminant analysis (LDA), with 10-fold cross-validation for binary classification based on clinical score thresholds. The LDA classifier accurately distinguished impairment categories, achieving area under the receiver operating characteristic curve (AUC-ROC) scores of 0.897 ± 0.272 for FMA-UE > 33, 0.981 ± 0.103 for FMA-UE > 44, 0.890 ± 0.262 for MAS > 0, 0.968 ± 0.130 for BRS > 3, and 0.987 ± 0.085 for BRS > 4. Notably, resting-state EMG alone yielded comparable classification performance. These findings demonstrate that EMG-driven assessments can reliably classify motor impairment levels, offering a pathway to objective clinical scoring that can streamline rehabilitation workflows, reduce therapists' manual burden, and prioritize patient recovery over assessment procedures.