Type de document
Études primaires
Année de publication
2023
Langue
Anglais
Titre de la revue
Journal of Electromyography and Kinesiology
Résumé
Background
Multijoint EMG-assisted optimization models are reliable tools to predict muscle forces as they account for inter- and intra-individual variations in activation. However, the conventional method of normalizing EMG signals using maximum voluntary contractions (MVCs) is problematic and introduces major limitations. The sub-maximal voluntary contraction (SVC) approaches have been proposed as a remedy, but their performance against the MVC approach needs further validation particularly during dynamic tasks.
Methods
To compare model outcomes between MVC and SVC approaches, nineteen healthy subjects performed a dynamic lifting task with two loading conditions.
Results
Results demonstrated that these two approaches produced highly correlated results with relatively small absolute and relative differences (<10 %) when considering highly-aggregated model outcomes (e.g. compression forces, stability indices). Larger differences were, however, observed in estimated muscle forces. Although some model outcomes, e.g. force of abdominal muscles, were statistically different, their effect sizes remained mostly small (ŋ2G ≤ 0.13) and in a few cases moderate (ŋ2G ≤ 0.165).
Conclusion
The findings highlight that the MVC calibration approach can reliably be replaced by the SVC approach when the true MVC exertion is not accessible due to pain, kinesiophobia and/or the lack of proper training.
Mots-clés
Électromyographie, Electromyography, Épreuve de la force musculaire, Muscle testing, Soulèvement des charges, Manual lifting, Contractilité musculaire, Muscle contractor activity, Rachis lombaire, Lumbar column
Numéro de projet IRSST
n/a
Citation recommandée
Eskandari, A. H., Ghezelbash, F., Shirazi-Adl, A., Gagnon, D., Mecheri, H. et Larivière, C. (2023). Validation of an EMG submaximal method to calibrate a novel dynamic EMG-driven musculoskeletal model of the trunk: Effects on model estimates. Journal of Electromyography and Kinesiology, 68, article 102728. https://doi.org/10.1016/j.jelekin.2022.102728
