Key points are not available for this paper at this time.
Accurate detection of user intention is a critical requirement for intelligent control systems in upper-limb rehabilitation robots. However, electromyography (EMG)-based recognition can degrade significantly under muscle fatigue. To address this limitation, we propose a hybrid EMG-electroencephalography (EEG) control framework that adaptively fuses peripheral (EMG) and central (EEG) biosignals for robust classification of elbow flexion and extension tasks. The system integrates a support vector machine (SVM)-based EMG classifier and a Common Spatial Pattern (CSP)-SVM EEG classifier, combined through a Bayesian fusion strategy whose weights are modulated in real time according to fatigue levels estimated from EMG spectral features via a k-nearest neighbors (k-NN) model. The hybrid framework was deployed on a lightweight robotic rehabilitation platform and evaluated with five healthy participants (3 females, age 26-39). Results show that adaptive fusion significantly outperformed unimodal baselines, achieving 94.5% classification accuracy (vs. 88.5% for EMG-only) with an end-to-end latency below 500 ms. Importantly, the fatigue-aware weighting preserved performance during high-fatigue conditions (91.4% vs. 83.1% for EMG-only), enhancing system robustness during prolonged sessions. These findings demonstrate the feasibility of a scalable, real-time, fatigue-adaptive control strategy with strong potential for clinical stroke rehabilitation and motor recovery.
Abdallah et al. (Wed,) studied this question.