Experimental parameter identification of a multi-scale musculoskeletal model controlled by electrical stimulation

• Verfasst von: Benoussaad, Mourad [VerfasserIn]
• Verfasst von: Poignet, Philippe [VerfasserIn]
• Verfasst von: Hayashibe, Mitsuhiro [VerfasserIn]
• Verfasst von: Azevedo-Coste, Christine [VerfasserIn]
• Verfasst von: Fattal, Charles [VerfasserIn]
• Verfasst von: Guiraud, David [VerfasserIn]
• Titel: Experimental parameter identification of a multi-scale musculoskeletal model controlled by electrical stimulation
• Titelzusatz: application to patients with spinal cord injury
• Verf.angabe: Mourad Benoussaad, Philippe Poignet, Mitsuhiro Hayashibe, Christine Azevedo-Coste, Charles Fattal, David Guiraud
• E-Jahr: 2013
• Jahr: 05 February 2013
• Umfang: 15 S.
• Fussnoten: Gesehen am 23.11.2020
• Titel Quelle: Enthalten in: Medical & biological engineering & computing
• Ort Quelle: [S.l.], 1963
• Jahr Quelle: 2013
• Band/Heft Quelle: 51(2013), 6, Seite 617-631
• ISSN Quelle: 1741-0444
• DOI: doi:10.1007/s11517-013-1032-y
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1740370481

Abstract

We investigated the parameter identification of a multi-scale physiological model of skeletal muscle, based on Huxley’s formulation. We focused particularly on the knee joint controlled by quadriceps muscles under electrical stimulation (ES) in subjects with a complete spinal cord injury. A noninvasive and in vivo identification protocol was thus applied through surface stimulation in nine subjects and through neural stimulation in one ES-implanted subject. The identification protocol included initial identification steps, which are adaptations of existing identification techniques to estimate most of the parameters of our model. Then we applied an original and safer identification protocol in dynamic conditions, which required resolution of a nonlinear programming (NLP) problem to identify the serial element stiffness of quadriceps. Each identification step and cross validation of the estimated model in dynamic condition were evaluated through a quadratic error criterion. The results highlighted good accuracy, the efficiency of the identification protocol and the ability of the estimated model to predict the subject-specific behavior of the musculoskeletal system. From the comparison of parameter values between subjects, we discussed and explored the inter-subject variability of parameters in order to select parameters that have to be identified in each patient.