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10:40   Musculoskeletal System Chair: Bart Koopman 10:40 15 mins MULTISCALE MODELING OF BONE ADAPTATION AT THE ORGAN, TISSUE AND CELL LEVELS Michele Colloca, Christian Hellmich, Keita Ito, Bert van Rietbergen Abstract: Load-adaptive bone remodeling algorithms implementing micro-FE models to represent the bone structure and calculate local loading conditions (Huiskes et al., 2000) can explain many features of bone adaptation at the tissue- and cell-level (Ruimermann et al., 2005). The application of these algorithms for patient-specific predictions, however, is limited by the high computational costs and the fact that at most sites (e.g. hip and spine) it is not possible to measure the bone structure in vivo. An alternative method that can reduce the computational time and does not require microstructural measurements in vivo is proposed in this study. We developed a multiscale analytical model to predict changes in bone density due to changes in cell activity or loading by combining, in a rigorous way, a micromechanical formulation of the mechanical stimulus at the tissue and cell level and a non linear differential equation expressing the density evolution of the bone remodeling system at the organ level. We used a representative volume element (RVE) of trabecular bone composed of a two-phase material: bone matrix (modeled as transversely isotropic material, Malandrino et al. 2012) and cylindrical voids. This assumption allowed for finding a closed-form solution for the mechanical stimulus sensed by the osteocytes, that is, the micromechanics-derived strain energy density based on an Eshelby matrix-inclusion problem. Hence, the typical RVE (mm) and pore (µm) scales were linked to predict the stress state on the trabecular surface where the bone remodeling takes place. The analytical evolution of the bone volume fraction was compared to that predicted by a corresponding numerical model, based on the previously validated micro-FE algorithm of bone remodeling. Good agreement was found with a difference of less than 2.4% while the computational time was dramatically reduced by a factor of almost one million. We thus expect that the proposed model can provide an efficient tool for simulating patient-specific bone remodeling at the organ level depending on changes in cell activity, material properties or loading conditions at lower levels. 10:55 15 mins INVERSE-INVERSE DYNAMICS OF HUMAN GAIT BASED ON GAIT FEATURES René Fluit, Marjolein van der Krogt, Nico Verdonschot, Bart Koopman Abstract: To predict the functional outcome of orthopaedic interventions on the musculo-skeletal model, we developed an inverted inverse dynamic model in order to predict whether and how patients walk postoperatively. In an inverse-inverse model, both the inverse dynamics input and the problem of unknown muscle forces are solved by formulating a minimization function[1]. Hence, the motion and muscle forces are optimized simultaneously. Gait lab measurements of ten healthy subjects were performed at the Radboud University Medical Centre. Three gait trials at comfortable speed of each subject, 30 in total, were analysed using a musculoskeletal analysis tool (AnyBody 4.2.1, AnyBody Technology A/S). Then, following Schwartz and Rozumalski (2008)[2], for each gait trial, all inverse-dynamic input over a complete gait cycle, i.e. all joint angles, pelvis position, ground reaction forces and center of pressure, were arranged in a single gait vector g. 30 singular vectors fk, referred to as gait features, were obtained by computing the singular value decomposition of the matrix G containing all concatenated gait vectors g. The inverse dynamics input was defined as an mth order approximation by multiplying the m main gait features fk with the feature components ck. In an inverse-inverse dynamic simulation, a complete gait cycle was predicted by optimizing only m feature components ck, based on an energy-cost function and subject to the constraint that the muscle activity should stay below 100 percent activation. The first gait feature described the average gait pattern, the following gait features described the variability present in the measured gait patterns. By optimizing only the first 5 or 10 gait features, already 79.5 and 90.3 percent of the variability was explained, respectively. The method does not guarantee that the optimized gait pattern is dynamically consistent, a situation in which unrealistically large residual forces are applied to the model. To prevent this, the ground reaction forces could be estimated in a separate optimization or the residual forces, together with the energy, could be minimized in a multi-objective optimization. Within the TLEMsafe project, software is developed to quickly generate subject-specific musculo-skeletal models. Combining this software with the inverse-inverse method may be an important step towards predicting the outcomes of orthopaedic surgeries. 11:10 15 mins TIME VARIANT IDENTIFICATION OF NONLINEAR PASSIVE ELASTICITY OF THE HUMAN ANKLE Stijn Van Eesbeek, Erwin de Vlugt, Frans van der Helm, Michel Verhaegen Abstract: By making use of haptic manipulators and system identification techniques parameters describing the neural and mechanical contributions to joint impedance can be obtained [1] These parameters are useful in rehabilitation practice for diagnostics and treatment selection [2]. Linear system theory has been used extensively for this purpose, but is no longer valid during functional tasks as parameters describing the neuromuscular system are known to vary with state, i.e. torque and angle, and task. Time-variant identification techniques have been developed which can potentially be used to identify human joint impedance through its complete range of motion out of a single observation [3]. This makes the method suitable for evaluation of joint impedance during functional tasks and appropriate for clinical application where short duration of the experiments is highly desired. Time-variant system descriptions are obtained with a LPV-subspace identification routine, where prescribed scheduling functions are used to give the model freedom to vary in time. Earlier studies under isometric conditions have shown a strong dependency of joint stiffness on exerted torque, using a single scheduling function based on torque. To have a valid system description of the joint undergoing large rotations the nonlinear elasticity of passive tissues has to be incorporated. The nonlinear passive stiffness can be incorporated in a LPV model by expanding the number of scheduling functions, in this study based on polynomials of the joint angle. Simulation studies have been used to show the validity of the proposed method. Subsequently, the method was applied on a batch of subjects. Torque perturbations were applied to the subject’s ankle using a haptic manipulator while the subject was moving through a large range of motion. For all subjects, the nonlinear passive elasticity could be retrieved out of a single observation using the proposed method. This opens many possibilities for identifying neural and non-neural components of joint impedance during tasks with large joint rotations. 11:25 15 mins SUBJECT-SPECIFIC MUSCULO-SKELETAL MODELS OF LOWER EXTREMITY BASED ON MEDICAL IMAGING AND FUNCTIONAL SCALING Vincenzo Carbone, Marjolein van der Krogt, Lara Vigneron, Jan Schepers, Sjoerd Kolk, Bart Koopman, Nico Verdonschot Abstract: Musculo-skeletal (MS) models represent a promising tool to predict the effects of surgery on individual patients. Unfortunately, MS geometry and musculo-tendon (MT) parameters, which greatly affect model force predictions, are difficult to measure directly; moreover, several parameters are known to vary with age, gender and activity. The aim of this study is to create subject-specific models of lower extremity, using medical imaging analysis techniques and functional scaling, in order to increase reliability of model force predictions. We use the Twente Lower Extremity Model (TLEM) [1] implemented in the AnyBody Modeling System (http://www.anybodytech.com/). TLEM consists of 12 body segments, 11 joints and 21 DOFs. Each leg contains 163 Hill-type MT element, representing 58 MT parts. •MS geometry is obtained using several medical imaging analysis techniques: bone contours and muscle volumes are semi-automatically segmented from MRI scans, muscle attachment sites and bony landmarks are estimated using an automatic morphing. •Functional scaling of MT parameters is based on dynamometer measurements during isometric and isokinetic maximal voluntary contractions (MVC): tendon slack length, optimal muscle fiber length and maximal isometric muscle force are optimized so that the model reproduces the measured subject-specific strength profiles. Image-based MS geometry and functionally scaled MT parameters have shown their potential in achieving more reliable model outcomes than simple anthropometric scaling, respectively, reducing hip reaction forces by 20% during deep knee bend [2] and muscle activity from 300% to 100% during maximal knee flexion torque [3]. Combining these two methods would permit to further reduce errors in muscle force predictions, improving the preciseness of subject-specific models and achieving the reliability necessary in surgical scenarios. 11:40 15 mins CHARACTERIZING SOFT-TISSUE MATERIAL PROPERTIES OF THE PLANTAR TISSUE FROM CT DATA AND FINITE ELEMENT ANALYSIS Paul Klaver, Michiel Oosterwaal, Lodewijk van Rhijn, Cees Oomens, Kenneth Meijer Abstract: Recently, a new foot and ankle model has been developed (the Glasgow/Maastricht model), which can predict the effect of an insole during dynamic loading conditions [1]. The model can be made patient-specific by measuring the anatomical structures from CT scans. The mechanical properties of the plantar soft tissue then can be estimated by changing the properties in the model until good agreement between the deformations predicted by the model and measured from CT scans during loading is obtained. The purpose of this study was to test if this procedure can provide realistic results. For seven patients suffering from metatarsalgia and ten healthy participants CT scans of the foot were made [2]. Three CT scans of the subjects loaded right foot and one CT scan of the unloaded right lower leg were made. During loading, plantar pressure was measured with a pedar insole (Novel gmbh, Munich, Germany). The CT images were segmented with MIMICS (Materialise, Leuven, Belgium) and a 2D finite element mesh of the unloaded situation was generated (3-matic, Materialise, Leuven, Belgium). The force measured in the experiment was applied as boundary condition to the model using the finite element program FEBio [3]. The material properties of the tissue in the model then were updated until best agreement was found between experimental and model deformations. The preliminary results obtained in this study demonstrate that the estimation of material properties using this backward approach is feasible. A finite element model simulating a loaded foot was created with the Ogden model describing the soft tissue. Values for the model were taken from literature [4]. Simulation of the plantar pressure using this model showed an error of just 1 kPa in part of the heel in comparison to the pressure measurements in the loaded situation. However, in the other parts of the foot the pressure estimation was not that close, so the values taken from literature appear not suitable enough to describe soft tissue. In future work several components such as plantar fascia and the Achilles tendon will be added in order to get a better pressure distribution along the plantar tissue. The optimization routine will be used to find the material properties that result in the best agreement between CT data and the model, and are therefore more suitable to describe soft tissue. Also the validity of this method applied on the 3D situation will be studied in further research. 1. Oosterwaal M, Telfer S, Carbes S, Torholm S, Meijer K, Woodburn J, Rhijn Lv: Generation of a dataset to develop a subject-specific, multibody, finite element, dynamic foot model. In EUROMECH Colloquium 511. Ponta Delgada, Azores, Portugal; 2011. 2. Oosterwaal M, Telfer S, Torholm S, Carbes S, van Rhijn LW, Macduff R, Meijer K, Woodburn J: Generation of subject-specific, dynamic, multisegment ankle and foot models to improve orthotic design: a feasibility study. BMC Musculoskelet Disord 2011, 12:256. 3. Maas SA, Ellis BJ, Ateshian GA, Weiss JA: FEBio: Finite Elements for Biomechanics. Journal of Biomechanical Engineering, 134(1):011005, 2012 4. Erdemir A, Viveiros ML, Ulbrecht JS, Cavanagh PR: An inverse finite-element model of the heel-pad indentation. Journal of biomechanics, 39(7):1279-1286, 2006 11:55 15 mins THE GLASGOW-MAASTRICHT FOOT MODEL- A 26 SEGMENT FOOT Michiel Oosterwaal, Sylvain Carbes, Scott Telfer, Lodewijk van Rhijn, Søren Tørholm, Kenneth Meijer Abstract: Standard gait analysis considers the foot as one rigid segment. While this approach is sufficient when focusing on hips or knees, studies of foot disease and injuries require more detailed models. Several multisegmental foot models have been proposed, up to 11 segments. However to our knowledge no rigid-body biomechanical foot model representing all the 26 foot bones has been developed to this day; neither exists an adequate marker protocol able to provide the relevant motion capture data necessary to construct, validate and use this model. In this study a 26 segments model is developed and used for inverse dynamic simulations. The model contains all intrinsic foot muscles and major plantar ligaments, as well as the needed joints and kinematic constraints to link properly the 26 segments. 25 subjects have been measured via a novel protocol, involving anatomical, biomechanical and clinical measurements. Kinematic and kinetic measurements have been done with 41 reflective markers, force plate and pressure plate. Via inverse dynamics, muscle function and joint reaction is computed using the AnyBody Modeling System (AnyBody Technology A/S, Aalborg, Denmark). A 46 DoF foot and ankle model is developed, based on 1 subject. This model is scaled and validated for the other subjects. The kinematic model is compared with bone pin studies in literature and showed good comparison. Measured talonavicular plantar flexion ROM during gait is 10 degrees and model prediction is 9 degrees. Similarly for calcaneocuboid plantar flexion the measured ROM is 10.5 degrees and model prediction is 8.5 degrees. Kinetic validation using measured EMG data is currently conducted. This study shows the development of a new biomechanical foot and ankle model. Further validation will be performed using CT images of the subjects.