Academic Journal
Personalized EMG preprocessing and normalization for musculoskeletal simulation.
| Τίτλος: | Personalized EMG preprocessing and normalization for musculoskeletal simulation. |
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| Συγγραφείς: | Cicėnas D; Department of Biomechanical Engineering, Faculty of Mechanics, Vilnius Gediminas Technical University, Vilnius, Lithuania., Žižienė J; Department of Biomechanical Engineering, Faculty of Mechanics, Vilnius Gediminas Technical University, Vilnius, Lithuania., Daunoravičienė K; Department of Biomechanical Engineering, Faculty of Mechanics, Vilnius Gediminas Technical University, Vilnius, Lithuania. |
| Πηγή: | Technology and health care : official journal of the European Society for Engineering and Medicine [Technol Health Care] 2026 May; Vol. 34 (3), pp. 371-391. Date of Electronic Publication: 2026 Mar 25. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: IOS Press Country of Publication: United States NLM ID: 9314590 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1878-7401 (Electronic) Linking ISSN: 09287329 NLM ISO Abbreviation: Technol Health Care Subsets: MEDLINE |
| Imprint Name(s): | Publication: Amsterdam : IOS Press Original Publication: Amsterdam ; New York : Elsevier, c1993- |
| Ιατρικοί όροι (MeSH): | Electromyography*/methods , Muscle, Skeletal*/physiology , Signal Processing, Computer-Assisted*, Muscle Contraction/physiology ; Range of Motion, Articular/physiology ; Elbow Joint/physiology ; Humans ; Biomechanical Phenomena ; Male ; Adult ; Female ; Computer Simulation ; Young Adult |
| Περίληψη: | BackgroundAccurate 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.ObjectiveThis 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.MethodsEMG 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.ResultsThe 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.ConclusionThe 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. |
| Competing Interests: | Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. |
| Contributed Indexing: | Keywords: EMG; OpenSim; adaptive filtering; muscle weighting; musculoskeletal simulation; signal normalization |
| Entry Date(s): | Date Created: 20260325 Date Completed: 20260713 Latest Revision: 20260713 |
| Update Code: | 20260714 |
| DOI: | 10.1177/09287329261432854 |
| PMID: | 41879215 |
| Βάση Δεδομένων: | MEDLINE |
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