Introduction. This research aims to enhance the control of intricate musculoskeletal spine models, a critical tool for comprehending both healthy and pathological spinal conditions. State-of-the-art musculoskeletal spine models incorporate segments for all vertebra, each possessing 3 degrees-of-freedom (DOF). Manually defining the posture with this amount of DOFs presents a significant challenge. The prevalent method of equally distributing the spine's overall rotation among the vertebrae often proves to be an inadequate assumption, particularly when dealing with the entire spine. Method. We have engineered a comprehensive non-linear spine rhythm and the requisite tools for its implementation in widely utilized musculoskeletal modelling software (1). The rhythm controls lateral bending, axial rotation, and flexion/extension. The mathematical and implementation details of the rhythm are beyond this abstract, but it's noteworthy that the implementation accommodates non-linear rhythms. This means, for example, that one set of rhythm coefficients is used for flexion and another for extension. The rhythm coefficients, which distinguish the movement along the spine, were derived from a review of spine literature. The values for spine and vertebra range-of-motion (ROM) vary significantly in published studies, and no complete dataset was found in any single study. Consequently, the rhythm presented here is a composite, designed to provide the most consistent and average set of rhythm coefficients. Result. The novel spine rhythm simplifies the control of detailed spine models, accommodating varying amounts of input data. It allows for the specification of only the overall motion or the posture at a more detailed level (i.e., lumbar, thoracic, neck). The tools and rhythm coefficients are publicly available on GitHub. Conclusion. The innovative spine rhythm enhances the usability of cutting-edge spine models. For flexion/extension of the spine, it introduces a non-linear rhythm, exhibiting distinct behaviour between flexion and extension - a feature not previously observed in musculoskeletal spine models. 1) The AnyBody Modeling System

The tendency towards using inertial sensors for remote monitoring of the patients at home is increasing. One of the most important characteristics of the sensors is sampling rate. Higher sampling rate results in higher resolution of the sampled signal and lower amount of noise. However, higher sampling frequency comes with a cost. The main aim of our study was to determine the validity of measurements performed by low sampling frequency (12.5 Hz) accelerometers (SENS) in patients with knee osteoarthritis compared to standard sensor-based motion capture system (Xsens). We also determined the test-retest reliability of SENS accelerometers. Participants were patients with unilateral knee osteoarthritis. Gait analysis was performed simultaneously by using Xsens and SENS sensors during two repetitions of over-ground walking at a self-selected speed. Gait data from Xsens were used as an input for AnyBody musculoskeletal modeling software to measure the accelerations at the exact location of two defined virtual sensors in the model (VirtualSENS). After preprocessing, the signals from SENS and VirtualSENS were compared in different coordinate axes in time and frequency domains. ICC for SENS data from first and second trials were calculated to assess the repeatability of the measurements. We included 32 patients (18 females) with median age 70.1[48.1 – 85.4]. Mean height and weight of the patients were 173.2 ± 9.6 cm and 84.2 ± 14.7 kg respectively. The correlation between accelerations in time domain measured by SENS and VirtualSENS in different axes was r = 0.94 in y-axis (anteroposterior), r = 0.91 in x-axis (vertical), r = 0.83 in z-axis (mediolateral), and r = 0.89 for the magnitude vector. In frequency domain, the value and the power of fundamental frequencies (F. 0. ) of SENS and VirtualSENS signals demonstrated strong correlation (r = 0.98 and r = 0.99 respectively). The result of test-retest evaluation showed excellent repeatability for acceleration measurement by SENS sensors. ICC was between 0.89 to 0.94 for different coordinate axes. Low sampling frequency accelerometers can provide valid and reliable measurements especially for home monitoring of the patients, in which handling big data and sensors cost and battery lifetime are among important issues

Introduction and Objective. The human body is designed to walk in an efficient way. As energy can be stored in elastic structures, it is no surprise that the strongest elastic structure of the human body, the iliofemoral ligament (IFL), is located in the lower limb. Numerous popular surgical hip interventions, however, affect the structural integrity of the hip capsule and there is a growing evidence that surgical repair of the capsule improves the surgical outcome. Though, the exact contribution of the iliofemoral ligament in energy efficient hip function remains unelucidated. Therefore, the objective of this study was to evaluate the influence of the IFL on energy efficient ambulation. Materials and Methods. In order to assess the potential passive contribution of the IFL to energy efficient ambulation, we simulated walking using the large public dataset (n=50) from Schreiber in a the AnyBody musculoskeletal modeling environment with and without the inclusion of the IFL. The work required from the psoas, iliacus, sartorius, quadriceps and gluteal muscles was evaluated in both situations. Considering the large uncertainty on ligament properties a parameter study was included. Results. A significant reduction in the active component of all hip flexors was observed when the IFL is intact. The required muscle work was found to be reduced by as much as 48% (CI: 29–62%), 61% (CI: 35–84%) and 38% (CI: 2–69%) for the psoas, iliacus, and sartorius muscle respectively. The IFL inclusion has no major effect on the required work from the quadriceps and the gluteal muscle group. The energy storage in the IFL is largest at maximal hip extension and the contribution to forward motion is the largest at the start of the swing phase. Conclusions. The iliofemoral ligament seems to be a crucial structure in energy efficient walking. The findings support need for meticulous reconstruction of the capsule ligament in case of surgical damage

Introduction. A deep squat (DS) is a challenging motion at the level of the hip joint generating substantial reaction forces (HJRF). During DS, the hip flexion angle approximates the functional range of hip motion. In some hip morphologies this femoroacetabular conflict has been shown to occur as early as 80° of hip flexion. So far in-vivo HJRF measurements have been limited to instrumented hip implants in a limited number of older patients performing incomplete squats (< 50° hip flexion and < 80° knee flexion). Clearly, young adults have a different kinetical profile with hip and knee flexion ranges going well over 100 degrees. Since hip loading data on this subgroup of the population is lacking and performing invasive measurements would be unfeasible, this study aimed to report a personalised numerical model solution based on inverse dynamics to calculate realistic in silico HJRF values during DS. M&M. Fifty athletic males (18–25 years old) were prospectively recruited for motion and morphological analysis. DS motion capture (MoCap) acquisitions and MRI scans of the lower extremities with gait lab marker positions were obtained. The AnyBody Modelling System (v6.1.1) was used to implement a novel personalisation workflow of the AnyMoCap template model. Bone geometries, semi-automatically segmented from MRI, and corresponding markers were incorporated into the template human model by an automated nonlinear morphing. Furthermore, a state-of-the-art TLEM 2.0 dataset, included in the Anybody Managed Model Repository (v2.0), was used in the template model. The subject-specific MoCap trials were processed to compute squat motion by resolving an overdeterminate kinematics problem. Inverse dynamics analyses were carried out to compute muscle and joint reaction forces in the entire body. Resulting hip joint loads were validated with measured in-vivo data from Knee bend trials in the OrthoLoad library. Additionally, anterior pelvic tilt, hip and knee joint angles were computed. Results. A preliminary set of results (20 out of 50 subjects) was analysed. The average HJRF was 3.42 times bodyweight at the peak of DS (95% confidence interval: 2.99 – 3.85%BW). Maximal hip and knee flexion angles were 113° (109.7°–116.8°) and 116° (109.4 – 123.0°) respectively. The anterior pelvic tilt demonstrated a biphasic profile with peak value of 33° (28.1° – 38.4°). Discussion. A non-invasive and highly personalised alternative for determining hip loading was presented. Consistently higher HJR forces during DS in young adults were demonstrated as opposed to the Orthoload dataset. Similarly, knee and hip flexion angles were much higher, which could support the increase in HJRF. We can conclude that DS hip kinetics in young adults clearly differ from the typical total hip arthroplasty population

To date, the fixation of proximal humeral fractures with angular stable locking plates is still insufficient with mechanical failure rates of 18% to 35%. The PHILOS plate (DePuy Synthes, Switzerland) is one of the most used implants. However, this plate has not been demonstrated to be optimal; the closely symmetric plate design and the largely heterogeneous bone mineral density (BMD) distribution of the humeral head suggest that the primary implant stability may be improved by optimizing the screw orientations. Finite element (FE) analysis allows testing of various implant configurations repeatedly to find the optimal design. The aim of this study was to evaluate whether computational optimization of the orientation of the PHILOS plate locking screws using a validated FE methodology can improve the predicted primary implant stability. The FE models of nineteen low-density (humeral head BMD range: 73.5 – 139.5 mg/cm3) left proximal humeri of 10 male and 9 female elderly donors (mean ± SD age: 83 ± 8.8 years) were created from high-resolution peripheral computer tomography images (XtremeCT, Scanco Medical, Switzerland), using a previously developed and validated computational osteosynthesis framework. To simulate an unstable mal-reduced 3-part fracture (AO/OTA 11-B3.2), the samples were virtually osteotomized and fixed with the PHILOS plate, using six proximal screws (rows A, B and E) according to the surgical guide. Three physiological loading modes with forces taken from musculoskeletal models (AnyBody, AnyBody Technology A/S, Denmark) were applied. The FE analyses were performed with Abaqus/Standard (Simulia, USA). The average principal compressive strain was evaluated in cylindrical bone regions around the screw tips; since this parameter was shown to be correlated with the experimental number of cycles to screw cut-out failure (R2 = 0.90). In a parametric analysis, the orientation of each of the six proximal screws was varied by steps of 5 in a 5×5 grid, while keeping the screw head positions constant. Unfeasible configurations were discarded. 5280 simulations were performed by repeating the procedure for each sample and loading case. The best screw configuration was defined as the one achieving the largest overall reduction in peri-screw bone strain in comparison with the PHILOS plate. With the final optimized configuration, the angle of each screw could be improved, exhibiting significantly smaller average bone strain around the screw tips (range of reduction: 0.4% – 38.3%, mean ± SD: 18.49% ± 9.56%). The used simulation approach may help to improve the fixation of complex proximal humerus fractures, especially for the target populations of patients at high risk of failure

Background. A new knee simulator has been developed at Ghent University. This simulator provides the unique opportunity of evaluating the knee kinematics during activities of daily living. The simulator therefore controls the position of the ankle in the sagittal plane while keeping the hip at a fixed position. This approach provides full kinematic freedom to the knee. To evaluate and validate the performance of the simulator, the development of and comparison with a numerical simulation model is discussed in this paper. Methods. Both a two and three dimensional simulation model have been developed using the AnyBody Modelling System (AMS). In the two dimensional model, the knee joint is represented by a hinge. Similarly, the ankle and hip joint are represented by a hinge joint and a variable amplitude quadriceps and hamstrings force is applied. In line with this simulation model, a hinge model was created that could be mounted in the UGent knee simulator to evaluate the performance of the simulated model. The hinge model thereby performs a cyclic motion under varying simulated muscle loads while recording the ankle reaction forces. In addition to the two dimensional model, a three dimensional model has been developed. More specifically, a model is built of a sawbone leg holding a posterior stabilised single radius total knee implant. The physical sawbone model contains simplified medial and lateral collateral ligaments. In line with the boundary conditions of the UGent knee simulator, the simulated hip contains a single rotational degree of freedom and the ankle holds four degrees of freedom (three rotations, single translation). In the simulations, the knee is modelled using the force-dependent kinematics (FDK) method built in the AMS. This leaves the knee with six degrees of freedom that are controlled by the ligament tension in combination with the applied quadriceps load and shape of the implant. The physical sawbone model goes through five cycles in the UGent simulator using while recording the kinematics of the femur and tibia using a set of markers rigidly attached to the femur and tibia bone. The position of the implant with respect to the markers was evaluated by CT-scanning the sawbone model. Results and Discussion. In a first step, the reaction forces at the ankle in the 2D model were evaluated. The difference between the simulated and measured reaction force is limited and can be explained from a slight variation of the attachment point of the simulated muscle loads. For the 3D model, the kinematic patterns have been evaluated for both the simulation and physical model using Grood & Suntay definitions. The kinematic parameters display realistic trends, however, no exact match has been obtained for all parameters so far. The latter might be attributed to a number of simplifications in the simulation model as well as elastic deformation of the physical sawbone model. Conclusion. A three dimensional model of a knee implant in the UGent Knee Simulator has been developed. The simulated kinematic patterns appear realistic though no exact match with the measured patterns has been obtained. Future research will therefore focus on the development of a more realistic experimental and numerical model

Summary Statement. Simulated increases in body weight led to increased displacement, von Mises stress, and contact pressure in finite element models of the extended and flexed knee. Contact shifted to locations of typical medial osteoarthritis lesions in the extended knee models. Introduction. Obesity is commonly associated with increased risk of osteoarthritis (OA). The effects of increases in body weight and other loads on the stresses and strains within a joint can be calculated using finite element (FE) models. The specific effects for different individuals can be calculated using subject-specific FE models which take individual geometry and forces into account. Model results can then be used to propose mechanisms by which damage within the joint may initiate. Patients & Methods. Twelve subject-specific FE models (Abaqus 6.11) of three normal healthy subjects were created by combining geometry (3T T1-weighted MRI scans processed using Mimics 13.0, Geomagic Studio 11, and SolidWorks 2010) and load cases (Vicon and AMTI motion analysis data processed within AnyBody Technology Version 3.0 and Matlab R2007a). Model geometry included the femur and tibia (rigid bodies), tibial cartilage and femoral cartilage (E = 12 MPa, ν = 0.45), and menisci (E. circumferential. = 120 MPa, ν. circumferential. = 0.2; E. axial/radial. = 20 MPa, ν. axial/radial. = 0.3). The tibia was held fixed while loads were applied to the centre of mass of the femur. Frictional contact (µ = 0.02) was modelled between soft tissues. Of the twelve models, six were of extended knees and six were of mid-range flexed (∼50°) knees. Each of these six models represented a paired set: a “normal” model and an “increased-load” model. In the flexed knee “increased-load” models, loads were doubled; in the extended knee “increased-load” models, loads were increased to a standard 2000 N compressive load across the joint (approximately three to four times larger than the original loads). Maximum displacements, von Mises stresses, and contact pressures on the articulating tibial cartilage and femoral cartilage surfaces were calculated; results of the “normal” and “increased-load” models were compared. Results. Increasing the applied loads increased the maximum displacements, von Mises stresses, and contact pressures. Contact shifted anteriorly in the extended knee models to typical locations of medial OA cartilage lesions. No contact shift occurred in the flexed knee models; contact remained in typical locations of lateral OA cartilage lesions, but the contact area extended in all directions, and displacements, stresses, and pressures increased. Discussion/Conclusion. Comparing the “normal” and “increased-load” results suggested two potential mechanical mechanisms involved in osteoarthritic cartilage lesion development. Contact shifted to areas of previously-unloaded cartilage in the extended knee “increased-load” models. Cartilage has regional material properties, with stiffer cartilage in areas of frequent contact and loading; shifting contact to areas of less-stiff cartilage could damage the cartilage and lead to degenerative diseases such as OA. Contact did not shift in the flexed knee “increased-load” models. Instead, the displacements, stresses, and pressures increased while the centres of contact remained relatively stationary. If these contact variables increase beyond a threshold magnitude, the cartilage could be damaged, potentially leading to OA