de Zee, Mark

Heinen, Frederik

King, Mark

Lewis, Martin

Rasmussen, John

Sørensen, Søren Nørgaard

English

    Aalborg UniversitetParameter estimations of the hill model in subject-specific musculoskeletal modelshow many measurements do we need?de Zee, Mark; Heinen, Frederik; Sørensen, Søren Nørgaard; King, Mark; Lewis, Martin;Rasmussen, JohnPublication date:2017Document VersionAccepted author manuscript, peer reviewed versionLink to publication from Aalborg UniversityCitation for published version (APA):de Zee, M., Heinen, F., Sørensen, S. N., King, M., Lewis, M., & Rasmussen, J. (2017). Parameter estimations ofthe hill model in subject-specific musculoskeletal models: how many measurements do we need?. Abstract fromXVI International Symposium on Computer Simulation in Biomechanics, Gold Coast, Australia.General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            ? Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            ? You may not further distribute the material or use it for any profit-making activity or commercial gain            ? You may freely distribute the URL identifying the publication in the public portal ?Take down policyIf you believe that this document breaches copyright please contact us at vbn@aub.aau.dk providing details, and we will remove access tothe work immediately and investigate your claim.Downloaded from vbn.aau.dk on: December 25, 2020XVI International Symposium on Computer Simulation in Biomechanics  July 20h – 22nd 2017, Brisbane, Australia   PARAMETER ESTIMATIONS OF THE HILL MODEL IN SUBJECT-SPECIFIC MUSCULOSKELETAL MODELS: HOW MANY MEASUREMENTS DO WE NEED?  Mark de Zee1, Frederik Heinen1, Søren N. Sørensen2, Mark King3, Martin Lewis4 and John Rasmussen2  1 Department of Health Science and Technology, Aalborg University, Denmark 2 MAKE, Aalborg University, Denmark 3 School of Sport, Exercise and Health Sciences, Loughborough University, United Kingdom 4 School of Science and Technology, Nottingham Trent University, United Kingdom Email: mdz@hst.aau.dk, Web: http://personprofil.aau.dk/102065   INTRODUCTION Musculoskeletal models have been used for decades now to investigate fundamental ques-tions regarding our complex musculoskeletal system. Ten years ago, Erdemir et al. [1] wrote in his review that estimation of individual mus-cle forces during human movement also might contribute to improved diagnosis and manage-ment of neurological and orthopaedic condi-tions. What we see now is a transition from the fundamental research towards applications within the clinical field and the industry. The re-quirements for a successful transition are stud-ies reporting successful validation of musculo-skeletal models and the ability to create sub-ject-specific models.   The last couple of years a lot of focus has been on validation and the Grand Challenge Compe-tition [2] to predict in vivo knee loads really has facilitated this. The second requirement regard-ing the ability to create subject-specific models has been less in focus, while a high level of subject-specific detail is required for applica-tions within the clinical field.  Creating subject-specific models can be divided into two elements. The first element is making sure that the model represents the same geom-etry as the subject. With the present imaging technology, it is possible to create models with exact bone geometry as the real subject, see for a nice example Marra et al. [3]. The second element is making sure that the model repre-sents the muscular capacity of the subject or, in other words, trying to obtain the subject-specific parameters of the Hill muscle-tendon model typ-ically used in musculoskeletal models. This is not a straightforward process. Imaging cannot be used, because important parameters like tendon slack length cannot be measured. Moreover, there is no direct relationship be-tween the visible muscle morphology in images and the parameters in the phenomenological Hill model. Methods using measurements ob-tained from dynamometers have been de-scribed in the literature for parameter estimation of the Hill model of an individual person [4]. Creating a full dataset of, for example, the lower extremity takes much time and is very hard for the subject/patient. It would therefore be a large improvement if a good estimation of the Hill pa-rameters were obtained from a limited number of measurements.  The aim of this study is therefore to evaluate the predictability of a subject-specific musculo-skeletal model based on only isometric joint torque measurements of the whole lower ex-tremity based on a reduced data set and a full dataset.   METHODS One male long distance runner (height: 1.85 m, weight: 66.5 kg) was included in this study. In accordance with the regional ethical review committee, a series of maximal isometric joint torque experiments were performed on a dyna-mometer for the ankle, knee and hip flexors and extensors of the dominant leg. Seven evenly distributed isometric measurements were taken around the ankle joint for three different knee joint angles. The same was done for the knee joint for three different hip joint angles.  A lower extremity model was used based upon the TLEMsafe 2.0 model [5] using the AnyBody Modeling System (AMS) (AnyBody Technology A/S, Denmark). The model was geometrically scaled based on anthropometric measure-ments. Each experimental condition was mim-icked in the model to evaluate the joint strength of the model after which two optimization pro-cedures were conducted using the SNOPT op-timizer. The first optimization (Multi_opt) used the full dataset (around 70 isometric measure-ments) to minimize the difference between the experimental and simulated isometric joint strengths by changing the Hill parameters.  The second optimization (Single_opt) used only the reduced dataset (around 24 isometric measurements), where for example the ankle joint data for only one knee angle was used. The two optimization results were compared to a reference model (Ref) which was identical to the optimised model but with muscle-tendon pa-rameters from the standard scaling in AMS.  RESULTS AND DISCUSSION The first observation (see also Fig 1) is that both the Single_opt and Multi_opt leads to a much better subject-specific model than the Ref scaling. The normalized RMS error for Ref was about a factor 4 larger than either optimized model.    Fig 1: Isometric joint torque profiles for the plantar flexors, knee flexors and hip flexors of the: optimization based on a reduced data set (Single_opt), optimization based on a full dataset (Multi_opt), experimental data (Exp) and the reference AnyBody model (Ref). Looking at the sum of the total normalized RMS error, the Multi_opt was somewhat smaller than the Single_opt with 27% difference. However, this difference was mostly coming from errors in the estimations for the knee flexors using Sin-gle_opt. For the other joints the difference be-tween the Single_opt and Multi_opt were small. Figure 1 illustrates the differences for the plan-tar flexors, the knee flexors and hip flexors.  This could indicate that a reduced dataset is in-sufficient for predicting the muscle-tendon pa-rameters of the knee flexors and hip extensors, but is sufficient for the knee extensors and the muscles around the other joints. The reason is probably that the knee flexor torque is primarily produced by bi-articular muscles and the opti-mizer needs therefore more data with different hip joint angles in order to obtain a good fit.  CONCLUSIONS This study showed that estimating muscle-tendon parameters for the entire lower extremi-ty based on a reduced dataset is sufficient for most joint torques. However, it might not be suf-ficient for the knee flexors/hip extensors, and the insufficiency may be due to the influence of bi-articular muscles for these joints.  The outcome of this study is important infor-mation for trying to minimize the experimental pressure on the subject and also time. Howev-er, the whole experimental procedure even with a reduced dataset plus the optimization time still takes a long time, which limits at present the applications in a true clinical environment.  REFERENCES 1. Erdemir A, et al. Clin Biomech 22: 131-154, 2007. 2. Fregly BJ, et al. J. Orthop. Res., 30: 503–513, 2012. 3. Marra MA, et al. J Biomech Eng 137: 020904-020904, 2015. 4. Heinen F, et al. Proc Inst Mech Eng H. 230: 976–984, 2016. 5. Carbone V, et al. J Biomech. 48: 734-741, 2015. 

Parameter estimations of the hill model in subject-specific musculoskeletal models:how many measurements do we need?

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Parameter estimations of the hill model in subject-specific musculoskeletal models:how many measurements do we need?

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