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The study investigated the potential for obtaining more accurate spine joint reaction force (JRF) estimates from musculoskeletal models by incorporating dynamic stereo X-ray imaging (DSX)-based in vivo lumbar vertebral rotational and translational kinematics compared to generic, rhythm (RHY)-based kinematics, while also observing the influence of accompanying inputs: intervertebral segment stiffness and neutral state. A full-body OpenSim® musculoskeletal model, constructed by combining existing lower- and upper-body models, was driven based on one volunteer’s (female; age 25; 60.8 kg; 176 cm) anthropometrics and kinematics from a series of upright standing and straight-legged dynamic lifting tasks. The lumbar spine portion was modified in a step-wise manner to observe effects of: (1) RHY vs. DSX lumbar kinematics; (2) No disc (bushing) stiffness (NBS); generic, linear bushing stiffness (LBS); subject-specific nonlinear bushing stiffness (NLBS); (3) Upright standing (UP) vs. Supine (SUP) neutral state; (4) Weight lifted: 4.5 kg vs. 13.6 kg. L4L5 JRF from 24 model variations based on combinations of aforementioned parameters were compared. Rhythm-based kinematics without translational components tends to over-predict JRF (31% and 39% for compression and shear, respectively) compared to DSX-based kinematics. Additionally, differences due to accompanying passive stiffness and neutral state choice combinations were even larger (>50%), indicating heightened demand on the quality of these accompanying inputs. The study not only highlights model sensitivity to choices made regarding the three primary inputs—kinematics, passive stiffness and neutral state— separately, but also how interactions between these choices can result in significant variability in joint loading estimates.

Passive rotational stiffness of the osseo-ligamentous spine is an important input parameter for estimating in-vivo spinal loading using musculoskeletal models. These data are typically acquired from cadaveric testing. Increasingly, they are also estimated from subject-specific imaging-based finite element (FE) models, which are typically built from CT/MR data obtained in supine position and employ pure rotation kinematics. We explored the sensitivity of FE-based lumbar passive rotational stiffness to two aspects of functional in-vivo kinematics: (a) passive strain changes from supine to upright standing position, and (b) in-vivo coupled translation-rotation kinematics. We developed subject-specific FE models of four subjects’ L4L5 segments from supine CT images. Sagittally symmetric flexion was simulated in two ways: (i) pure flexion up to 12° under a 500 N follower load directly from the supine pose. (ii) First, a displacement-based approach was implemented to attain the upright pose, as measured using Dynamic Stereo X-ray (DSX) imaging. We then simulated in-vivo flexion using DSX imaging-derived kinematics. Datasets from weight-bearing motion with three different external weights [(4.5 kg), (9.1 kg), (13.6 kg)] were used. Accounting for supine-upright motion generated compressive pre-loads ≈ 468 N (±188 N) and a “pre-torque” ≈2.5 Nm (±2.2 Nm), corresponding to 25% of the reaction moment at 10° flexion (case (i)). Rotational stiffness estimates from DSX-based coupled translation-rotation kinematics were substantially higher compared to pure flexion. Reaction Moments were almost 90% and 60% higher at 5° and 10° of L4L5 flexion, respectively. Within-subject differences in rotational stiffness based on external weight were small, although between-subject variations were large.

Most clinical gait laboratories use the conventional gait analysis model. This model uses a computational method called Direct Kinematics (DK) to calculate joint kinematics. In contrast, musculoskeletal modelling approaches use Inverse Kinematics (IK) to obtain joint angles. IK allows additional analysis (e.g. muscle-tendon length estimates), which may provide valuable information for clinical decision-making in people with movement disorders. The twofold aims of the current study were: (1) to compare joint kinematics obtained by a clinical DK model (Vicon Plug-in-Gait) with those produced by a widely used IK model (available with the OpenSim distribution), and (2) to evaluate the difference in joint kinematics that can be solely attributed to the different computational methods (DK versus IK), anatomical models and marker sets by using MRI based models. Eight children with cerebral palsy were recruited and presented for gait and MRI data collection sessions. Differences in joint kinematics up to 13° were found between the Plug-in-Gait and the gait 2392 OpenSim model. The majority of these differences (94.4%) were attributed to differences in the anatomical models, which included different anatomical segment frames and joint constraints. Different computational methods (DK versus IK) were responsible for only 2.7% of the differences. We recommend using the same anatomical model for kinematic and musculoskeletal analysis to ensure consistency between the obtained joint angles and musculoskeletal estimates.

Obesity has been associated to increase the risk of low back disorders. Previous musculoskeletal models simulating the effect of body weight on intervertebral joint loads have assumed identical body postures for obese and normal-weight individuals during a given physical activity. Our recent kinematic-measurement studies, however, indicate that obese individuals adapt different body postures (segmental orientations) than normal-weight ones when performing load-reaching activities. The present study, therefore, used a subject- and kinematics-specific musculoskeletal modeling approach to compare spinal loads of nine normal-weight and nine obese individuals each performing twelve static two-handed load-reaching activities at different hand heights, anterior distances, and asymmetry angles (total of 12 tasks × 18 subjects = 216 model simulations). Each model incorporated personalized muscle architectures, body mass distributions, and full-body kinematics for each subject and task. Results indicated that even when accounting for subject-specific body kinematics obese individuals experienced significantly larger (by ∼38% in average) L5-S1 compression (2305 ± 468 N versus 1674 ± 337 N) and shear (508 ± 111 N versus 705 ± 150 N) loads during all reaching activities (p < 0.05 for all hand positions). This average difference of ∼38% was similar to the results obtained from previous modeling investigations that neglected kinematics differences between the two weight groups. Moreover, there was no significant interaction effect between body weight and hand position on the spinal loads; indicating that the effect of body weight on L5-S1 loads was not dependent on the position of hands. Postural differences alone appear, hence, ineffective in compensating the greater spinal loads that obese people experience during reaching activities.

Quantitative dynamic evaluation of spino-pelvic motion in subjects with spinal deformity using optical motion analysis is currently lacking. The aim of this study was to develop and validate subject-specific, thoracolumbar spine multi-body skeletal models for evaluating spino-pelvic kinematics in a spinal deformity population. A new workflow for creating subject-specific spino-pelvic models in a weight-bearing position through computed tomography (CT) and biplanar radiography is described. As part of a two-step validation process the creation of such a model was first validated against a ground truth CT reconstruction of a plastinated cadaver. Secondly, biplanar radiographic images of one healthy and 12 adult spinal deformity subjects were obtained in two standing positions: upright and bent. Two subject-specific models for each of these subjects were then created to represent both standing positions. The result of inverse kinematics solutions, simulating the specific bending motion using the upright models, are compared with the models created in bent position, quantifying the marker-based spino-pelvic tracking accuracy. The workflow created spinal deformity models with mean accuracies between 0.71–1.95 mm and 1.25–2.27° for vertebral positions and orientations, respectively. In addition, the mean marker-based spino-pelvic tracking accuracies were between 0.9–1.8 mm and 2.9–5.6° for vertebral positions and rotations, respectively. This study presented the first validated biplanar radiography-based method to generate subject-specific spino-pelvic, rigid body models that allows the inclusion of subject-specific bone geometries, the personalization of the 3D weight-bearing spinal alignment with accuracy comparable to clinically used software for 3D reconstruction, and the localization of external markers in spinal deformity subjects. This work will allow new concepts of dynamic functionality evaluation of patients with spinal deformity.

In finite element (FE) models knee ligaments can represented either by a group of one-dimensional springs, or by three-dimensional continuum elements based on segmentations. Continuum models closer approximate the anatomy, and facilitate ligament wrapping, while spring models are computationally less expensive. The mechanical properties of ligaments can be based on literature, or adjusted specifically for the subject. In the current study we investigated the effect of ligament modelling strategy on the predictive capability of FE models of the human knee joint. The effect of literature-based versus specimen-specific optimized material parameters was evaluated. Experiments were performed on three human cadaver knees, which were modelled in FE models with ligaments represented either using springs, or using continuum representations. In spring representation collateral ligaments were each modelled with three and cruciate ligaments with two single-element bundles. Stiffness parameters and pre-strains were optimized based on laxity tests for both approaches. Validation experiments were conducted to evaluate the outcomes of the FE models. Models (both spring and continuum) with subject-specific properties improved the predicted kinematics and contact outcome parameters. Models incorporating literature-based parameters, and particularly the spring models (with the representations implemented in this study), led to relatively high errors in kinematics and contact pressures. Using a continuum modelling approach resulted in more accurate contact outcome variables than the spring representation with two (cruciate ligaments) and three (collateral ligaments) single-element-bundle representations. However, when the prediction of joint kinematics is of main interest, spring ligament models provide a faster option with acceptable outcome.

Computational knee models that replicate the joint motion are important tools to discern difficult-to-measure functional joint biomechanics. Numerous knee kinematic models of different complexity, with either generic or subject-specific anatomy, have been presented and used to predict three-dimensional tibiofemoral (TFJ) and patellofemoral (PFJ) joint kinematics of cadavers or healthy adults, but not pediatric populations. The aims of this study were: (i) to develop subject-specific TFJ and PFJ kinematic models, with TFJ models having either rigid or extensible ligament constraints, for eight healthy pediatric participants and (ii) to validate the estimated joint and ligament kinematics against in vivo kinematics measured from magnetic resonance imaging (MRI) at four TFJ flexion angles. Three different TFJ models were created from MRIs and used to solve the TFJ kinematics: (i) 5-rigid-link parallel mechanism with rigid surface contact and isometric anterior cruciate (ACL), posterior cruciate (PCL) and medial collateral (MCL) ligaments (ΔLnull), (ii) 6-link parallel mechanism with minimized ACL, PCL, MCL and lateral collateral ligament (LCL) length changes (ΔLmin) and (iii) 6-link parallel mechanism with prescribed ACL, PCL, MCL and LCL length variations (ΔLmatch). Each model’s geometrical parameters were optimized using a Multiple Objective Particle Swarm algorithm. When compared to MRI-measured data, ΔLnull and ΔLmatch performed the best, with average root mean square errors below 6.93° and 4.23 mm for TFJ and PFJ angles and displacements, respectively, and below 2.01 mm for ligament lengths (<4.32% ligament strain). Therefore, within these error ranges, ΔLnull and ΔLmatch can be used to estimate three-dimensional pediatric TFJ, PFJ and ligament kinematics and can be incorporated into lower-limb models to estimate joint kinematics and kinetics during dynamic tasks.

The invention of the surgical robot enabled accurate component implantation during total hip arthroplasty (THA). However, a preoperative surgical planning methodology is still lacking to determine the acetabular cup alignment considering the patient-specific hip functions during daily activities such as walking. To simultaneously avoid implant edgeloading and impingement, this study established a kinematic–kinetic compliant (KKC) acetabular cup positioning method based on preoperative gait kinematics measurement and musculoskeletal modeling. Computed tomography images around the hip joint and their biomechanical data during gait, including motion tracking and foot–ground reaction forces, were collected. Using the reconstructed pelvic and femur geometries, the patient-specific hip muscle insertions were located in the lower limb musculoskeletal model via point cloud registration. The designed cup orientation has to be within the patient-specific safe zone to prevent implant impingement, and the optimized value selected based on the time-dependent hip joint reaction force to minimize the risk of edgeloading. As a validation of the proposed musculoskeletal model, the predicted lower limb muscle activations for seven patients were correlated with their surface electromyographic measurements, and the computed hip contact force was also in quantitative agreement with data from the literature. However, the designed cup orientations were not always within the well-known Lewinnek safe zone, highlighting the importance of KKC surgical planning based on patient-specific biomechanical evaluations.

Individuals with diabetes are at a higher risk of developing foot ulcers. To better understand internal soft tissue loading and potential treatment options, subject-specific finite element (FE) foot models have been used. However, existing models typically lack subject-specific soft tissue material properties and only utilize subject-specific anatomy. Therefore, this study determined subject-specific hindfoot soft tissue material properties from one non-diabetic and one diabetic subject using inverse FE analysis. Each subject underwent cyclic MRI experiments to simulate physiological gait and to obtain compressive force and three-dimensional soft tissue imaging data at 16 phases along the loading–unloading cycles. The FE models consisted of rigid bones and nearly-incompressible first-order Ogden hyperelastic skin, fat, and muscle (resulting in six independent material parameters). Then, calcaneus and loading platen kinematics were computed from imaging data and prescribed to the FE model. Two analyses were performed for each subject. First, the skin, fat, and muscle layers were lumped into a single generic soft tissue material and optimized to the platen force. Second, the skin, fat, and muscle material properties were individually determined by simultaneously optimizing for platen force, muscle vertical displacement, and skin mediolateral bulging. Our results indicated that compared to the individual without diabetes, the individual with diabetes had stiffer generic soft tissue behavior at high strain and that the only substantially stiffer multi-material layer was fat tissue. Thus, we suggest that this protocol serves as a guideline for exploring differences in non-diabetic and diabetic soft tissue material properties in a larger population.

Soft tissue artifact (STA) distort marker-based knee kinematics measures and make them difficult to use in clinical practice. None of the current methods designed to compensate for STA is suitable, but multi-body optimization (MBO) has demonstrated encouraging results and can be improved. The goal of this study was to develop and validate the performance of knee joint models, with anatomical and subject-specific kinematic constraints, used in MBO to reduce STA errors. Twenty subjects were recruited: 10 healthy and 10 osteoarthritis (OA) subjects. Subject-specific knee joint models were evaluated by comparing dynamic knee kinematics recorded by a motion capture system (KneeKG™) and optimized with MBO to quasi-static knee kinematics measured by a low-dose, upright, biplanar radiographic imaging system (EOS®). Errors due to STA ranged from 1.6° to 22.4° for knee rotations and from 0.8mm to 14.9mm for knee displacements in healthy and OA subjects. Subject-specific knee joint models were most effective in compensating for STA in terms of abduction–adduction, inter–external rotation and antero–posterior displacement. Root mean square errors with subject-specific knee joint models ranged from 2.2±1.2° to 6.0±3.9° for knee rotations and from 2.4±1.1mm to 4.3±2.4mm for knee displacements in healthy and OA subjects, respectively. Our study shows that MBO can be improved with subject-specific knee joint models, and that the quality of the motion capture calibration is critical. Future investigations should focus on more refined knee joint models to reproduce specific OA knee geometry and physiology.