Background Accurate interpretation of electromyography (EMG) signals is essential for reliable control of musculoskeletal (MS) models in biomechanics and rehabilitation applications. Conventional preprocessing methods may not account for subject-specific signal characteristics and task-related muscle function. Objective This study aimed to develop and validate an adaptive and personalized EMG preprocessing pipeline to enhance the physiological accuracy of EMG-driven musculoskeletal models during elbow flexion-extension tasks. Methods EMG signals from six upper limb muscles were recorded using a Delsys system while participants performed elbow flexion-extension movements. The signals were preprocessed using individualized spectral filtering and a dual-stage normalization approach. First, dynamic maximum voluntary contraction (MVC) based min–max normalization was applied to standardize signal amplitudes. Second, functional weighting was used to scale each muscle's activation based on its biomechanical contribution to the movement. The processed signals were used as input to an OpenSim elbow model, and resulting joint kinematics were compared to reference motion data captured by an Xsens system. Results The EMG-driven OpenSim model showed strong agreement with the Xsens data, with correlation coefficients exceeding 0.98 and root mean square error (RMSE) values below 8°. While a minor systematic offset was observed, joint angle trajectories remained consistent and physiologically plausible across trials. Conclusion The proposed subject-specific EMG preprocessing pipeline enhances the accuracy and interpretability of biomechanical models. Future research should explore adaptive signal alignment techniques and AI-based processing methods to improve model robustness in dynamic and wearable scenarios.
Cicėnas et al. (2026) studied this question.