title: Estimation of musculotendon parameters for scaled and subject specific musculoskeletal models using an optimization technique.
abstract: A challenging aspect of subject specific musculoskeletal modeling is the estimation of muscle parameters, especially optimal fiber length and tendon slack length. In this study, the method for scaling musculotendon parameters published by Winby et al. (2008), J. Biomech. 41, 1682-1688, has been reformulated, generalized and applied to two cases of practical interest: 1) the adjustment of muscle parameters in the entire lower limb following linear scaling of a generic model and 2) their estimation "from scratch" in a subject specific model of the hip joint created from medical images. In the first case, the procedure maintained the muscles׳ operating range between models with mean errors below 2.3% of the reference model normalized fiber length value. In the second case, a subject specific model of the hip joint was created using segmented bone geometries and muscle volumes publicly available for a cadaveric specimen from the Living Human Digital Library (LHDL). Estimated optimal fiber lengths were found to be consistent with those of a previously published dataset for all 27 considered muscle bundles except gracilis. However, computed tendon slack lengths differed from tendon lengths measured in the LHDL cadaver, suggesting that tendon slack length should be determined via optimization in subject-specific applications. Overall, the presented methodology could adjust the parameters of a scaled model and enabled the estimation of muscle parameters in newly created subject specific models. All data used in the analyses are of public domain and a tool implementing the algorithm is available at https://simtk.org/home/opt_muscle_par.
authors.affiliation: Centre for Musculoskeletal Research, School of Allied Health Sciences, Menzies Health Institute Queensland, Griffith University, Southport, Australia; Department of Mechanical Engineering, University of Sheffield, United Kingdom; INSIGNEO Institute for in silico Medicine, University of Sheffield, United Kingdom. Electronic address: l.modenese@sheffield.ac.uk.
authors.forename: Luca
authors.initials: L
authors.lastname: Modenese
authors.affiliation: Department of Management and Engineering, University of Padua, Vicenza, Italy.
authors.forename: Elena
authors.initials: E
authors.lastname: Ceseracciu
authors.affiliation: Department of Management and Engineering, University of Padua, Vicenza, Italy.
authors.forename: Monica
authors.initials: M
authors.lastname: Reggiani
authors.affiliation: Centre for Musculoskeletal Research, School of Allied Health Sciences, Menzies Health Institute Queensland, Griffith University, Southport, Australia.
authors.forename: David G
authors.initials: DG
authors.lastname: Lloyd
mesh_terms: D000465:Algorithms; D006621:Hip Joint / Q000502:physiology; D006801:Humans; D035002:Lower Extremity / Q000502:physiology; D008954:Models, Biological; D018482:Muscle, Skeletal / Q000502:physiology*; D013710:Tendons / Q000502:physiology*
keywords: Hip joint; Muscle models; Muscle–tendon parameters; Optimal fiber length; Parameter optimization; Scaling; Subject specific; Tendon slack length