MusIC method enhancement by a sensitivity study of its performance: application to a lower limbs musculoskeletal model

Abstract : Providing a biomechanical feedback during experimental sessions is a real outcome for rehabilitation, ergonomics or training applications. However, such applications imply a fast computation of the biomechanical quantities to be observed. The MusIC method has been designed to solve quickly the muscle forces estimation problem, thanks to a database interpolation. The current paper aims at enhancing its performance. Without generating any database, the method allows to identify optimal densities (number of samples contained in the database) with respect to the method accuracy and the off-line computation time needed to generate the database. On a lower limbs model (12 degrees of freedom, 82 muscles), thanks to this work, the MusIC method exhibits an accuracy error of 0.1% with an off-line computation time lower than 10 minutes. The on-line computation frequency (number of samples computed per second) is about 58 Hz. Thanks to these improvements, the MusIC method can be used to produce a feedback during an experimentation with a wide variety of musculoskeletal models or cost functions (used to share forces into muscles). The interaction between the subject, the experimenter (e.g. trainer, ergonomist or clin-ician) and the biomechanical data (e.g. muscle forces) in experimental sessions is a promising way to enhance rehabilitation, training or design techniques.
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Antoine Muller, Charles Pontonnier, Georges Dumont. MusIC method enhancement by a sensitivity study of its performance: application to a lower limbs musculoskeletal model. Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis, 2018, pp.1-13. ⟨hal-01904443⟩

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