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

ABSTRACT: 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 distinguish

SUBMITTER: Anastasiev A 

PROVIDER: S-EPMC12656085 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature