Consideration of multiple load cases is critical in modelling orthotropic bone adaptation in the femur

Functional adaptation of the femur has been investigated in several studies by embedding bone remodelling algorithms in finite element (FE) models, with simpli- fications often made to the representation of bone’s material symmetry and mechanical environment. An orthotropic strain-driven adaptation algorithm is proposed in order to predict the femur’s volumetric material property distribution and directionality of its internal structures within a continuum. The algorithm was applied to a FE model of the femur, with muscles, ligaments and joints included explicitly. Multiple load cases representing distinct frames of two activities of daily living (walking and stair climbing) were considered. It is hypothesised that low shear moduli occur in areas of bone that are simply loaded and high shear moduli in areas subjected to complex loading conditions. In addition, it is investigated whether material properties of different femoral regions are stimulated by different activities. The loading and boundary conditions were considered to provide a physiological mechanical environment. The resulting volumetric material property distribution and directionalities agreed with ex vivo imaging data for the whole femur. Regions where non-orthogonal trabecular crossing has been documented coincided with higher values of predicted shear moduli. The topological influence of the different activities modelled was analysed. The influence of stair climbing on the properties of the femoral neck region is highlighted. It is recommended that multiple load cases should be considered when modelling bone adaptation. The orthotropic model of the complete femur is released with this study

Effects of hip joint centre mislocation on gait kinematics of children with cerebral palsy calculated using patient-specific direct and inverse kinematic models

Joint kinematics can be calculated by Direct Kinematics (DK), which is used in most clinical gait laboratories, or Inverse Kinematics (IK), which is mainly used for musculoskeletal research. In both approaches, joint centre locations are required to compute joint angles. The hip joint centre (HJC) in DK models can be estimated using predictive or functional methods, while in IK models can be obtained by scaling generic models. The aim of the current study was to systematically investigate the impact of HJC location errors on lower limb joint kinematics of a clinical population using DK and IK approaches. Subject-specific kinematic models of eight children with cerebral palsy were built from magnetic resonance images and used as reference models. HJC was then perturbed in 6mm steps within a 60mm cubic grid, and kinematic waveforms were calculated for the reference and perturbed models. HJC perturbations affected only hip and knee joint kinematics in a DK framework, but all joint angles were affected when using IK. In the DK model, joint constraints increased the sensitivity of joint range-of-motion to HJC location errors. Mean joint angle offsets larger than 5° were observed for both approaches (DK and IK), which were larger than previously reported for healthy adults. In the absence of medical images to identify the HJC, predictive or functional methods with small errors in anterior-posterior and medial-lateral directions and scaling procedures minimizing HJC location errors in the anterior-posterior direction should be chosen to minimize the impact on joint kinematics

Investigation of the dependence of joint contact forces on musculotendon parameters using a codified workflow for image-based modelling

The generation of subject-specific musculoskeletal models of the lower limb has become a feasible taskthanks to improvements in medical imaging technology and musculoskeletal modelling software.Nevertheless, clinical use of these models in paediatric applications is still limited for what concernsthe estimation of muscle and joint contact forces. Aiming to improve the current state of the art, amethodology to generate highly personalized subject-specific musculoskeletal models of the lower limbbased on magnetic resonance imaging (MRI) scans was codified as a step-by-step procedure and appliedto data from eight juvenile individuals. The generated musculoskeletal models were used to simulate 107gait trials using stereophotogrammetric and force platform data as input. To ensure completeness of themodelling procedure, muscles’ architecture needs to be estimated. Four methods to estimate muscles’maximum isometric force and two methods to estimate musculotendon parameters (optimal fiber lengthand tendon slack length) were assessed and compared, in order to quantify their influence on the models’output. Reported results represent the first comprehensive subject-specific model-based characterizationof juvenile gait biomechanics, including profiles of joint kinematics and kinetics, muscle forces and jointcontact forces. Our findings suggest that, when musculotendon parameters were linearly scaled from areference model and the muscle force-length-velocity relationship was accounted for in the simulations,realistic knee contact forces could be estimated and these forces were not sensitive the method used tocompute muscle maximum isometric force

Biofeedback for gait retraining based on real-time estimation of tibiofemoral joint contact forces

Biofeedback assisted rehabilitation and intervention technologies have the potential to modify clinically relevant biomechanics. Gait retraining has been used to reduce the knee adduction moment, a surrogate of medial tibiofemoral joint loading often used in knee osteoarthritis research. In this study we present an electromyogram-driven neuromusculoskeletal model of the lower-limb to estimate, in real-time, the tibiofemoral joint loads. The model included 34 musculotendon units spanning the hip, knee, and ankle joints. Full-body inverse kinematics, inverse dynamics, and musculotendon kinematics were solved in real-time from motion capture and force plate data to estimate the knee medial tibiofemoral contact force (MTFF). We analyzed 5 healthy subjects while they were walking on an instrumented treadmill with visual biofeedback of their MTFF. Each subject was asked to modify their gait in order to vary the magnitude of their MTFF. All subjects were able to increase their MTFF, whereas only 3 subjects could decrease it, and only after receiving verbal suggestions about possible gait modification strategies. Results indicate the important role of knee muscle activation patterns in modulating the MTFF. While this study focused on the knee, the technology can be extended to examine the musculoskeletal tissue loads at different sites of the human body

Biofeedback for gait retraining based on real-time estimation of tibiofemoral joint contact forces

Biofeedback assisted rehabilitation and intervention technologies have the potential to modify clinically relevant biomechanics. Gait retraining has been used to reduce the knee adduction moment, a surrogate of medial tibiofemoral joint loading often used in knee osteoarthritis research. In this study we present an electromyogram-driven neuromusculoskeletal model of the lower-limb to estimate, in real-time, the tibiofemoral joint loads. The model included 34 musculotendon units spanning the hip, knee, and ankle joints. Full-body inverse kinematics, inverse dynamics, and musculotendon kinematics were solved in real-time from motion capture and force plate data to estimate the knee medial tibiofemoral contact force (MTFF). We analyzed 5 healthy subjects while they were walking on an instrumented treadmill with visual biofeedback of their MTFF. Each subject was asked to modify their gait in order to vary the magnitude of their MTFF. All subjects were able to increase their MTFF, whereas only 3 subjects could decrease it, and only after receiving verbal suggestions about possible gait modification strategies. Results indicate the important role of knee muscle activation patterns in modulating the MTFF. While this study focused on the knee, the technology can be extended to examine the musculoskeletal tissue loads at different sites of the human body

Commentary on the Integration of Model Sharing and Reproducibility Analysis to Scholarly Publishing Workflow in Computational Biomechanics

© 1964-2012 IEEE.Objective: The overall goal of this paper is to demonstrate that dissemination of models and analyses for assessing the reproducibility of simulation results can be incorporated in the scientific review process in biomechanics. Methods: As part of a special issue on model sharing and reproducibility in the IEEE Transactions on Biomedical Engineering, two manuscripts on computational biomechanics were submitted: Rajagopal et al., IEEE Trans. Biomed. Eng., 2016 and Schmitz and Piovesan, IEEE Trans. Biomed. Eng., 2016. Models used in these studies were shared with the scientific reviewers and the public. In addition to the standard review of the manuscripts, the reviewers downloaded the models and performed simulations that reproduced results reported in the studies. Results: There was general agreement between simulation results of the authors and those of the reviewers. Discrepancies were resolved during the necessary revisions. The manuscripts and instructions for download and simulation were updated in response to the reviewers' feedback; changes that may otherwise have been missed if explicit model sharing and simulation reproducibility analysis was not conducted in the review process. Increased burden on the authors and the reviewers, to facilitate model sharing and to repeat simulations, were noted. Conclusion: When the authors of computational biomechanics studies provide access to models and data, the scientific reviewers can download and thoroughly explore the model, perform simulations, and evaluate simulation reproducibility beyond the traditional manuscript-only review process. Significance: Model sharing and reproducibility analysis in scholarly publishing will result in a more rigorous review process, which will enhance the quality of modeling and simulation studies and inform future users of computational models

Reference standardization and triglyceride interference of a new homogeneous HDL-cholesterol assay compared with a former chemical precipitation assay

A homogeneous HDL-c assay (HDL-H), which uses polyethylene glycol-modified enzymes and sulfated alpha-cyclodextrin, was assessed for precision, accuracy, and cholesterol and triglyceride interference. In addition, its analytical performance was compared with that of a phosphotungstic acid (PTA)/MgCl2 precipitation method (HDL-P). Within-run CVs were < or = 1.87%; total CVs were < or = 3.08%. Accuracy was evaluated in fresh normotriglyceridemic sera using the Designated Comparison Method (HDL-H = 1.037 Designated Comparison Method + 4 mg/L; n = 63) and in moderately hypertriglyceridemic sera by using the Reference Method (HDL-H = 1.068 Reference Method - 17 mg/L; n = 41). Mean biases were 4.5% and 2.2%, respectively. In hypertriglyceridemic sera (n = 85), HDL-H concentrations were increasingly positively biased with increasing triglyceride concentrations. The method comparison between HDL-H and HDL-P yielded the following equation: HDL-H = 1.037 HDL-P + 15 mg/L; n = 478. We conclude that HDL-H amply meets the 1998 NCEP recommendations for total error; its precision is superior compared with that of HDL-P, and its average bias remains below +/-5% as long as triglyceride concentrations are < or = 10 g/L and in case of moderate hypercholesterolemia

Tibiofemoral Contact Forces in the Anterior Cruciate Ligament-Reconstructed Knee.

PURPOSE: To investigate differences in ACL reconstructed (ACLR) and healthy individuals in terms of the magnitude of the tibiofemoral contact forces, as well as the relative muscle and external load contributions to those contact forces, during walking, running and sidestepping gait tasks. METHODS: A computational electromyography-driven neuromusculoskeletal model was used to estimate the muscle and tibiofemoral contact forces in those with combined semitendinosus and gracilis tendon autograft ACLR (n=104, 29.7±6.5 years, 78.1±14.4 kg) and healthy controls (n=60, 27.5±5.4 years, 67.8±14.0 kg) during walking (1.4±0.2 ms), running (4.5±0.5 ms) and sidestepping (3.7±0.6 ms). Within the computational model, the semitendinosus of ACLR participants was adjusted to account for literature reported strength deficits and morphological changes subsequent to autograft harvesting. RESULTS: ACLRs had smaller maximum total and medial tibiofemoral contact forces (~80% of control values, scaled to bodyweight) during the different gait tasks. Compared to controls, ACLRs were found to have a smaller maximum knee flexion moment, which explained the smaller tibiofemoral contact forces. Similarly, compared to controls, ACLRs had both a smaller maximum knee flexion angle and knee flexion excursion during running and sidestepping, which may have concentrated the articular contact forces to smaller areas within the tibiofemoral joint. Mean relative muscle and external load contributions to the tibiofemoral contact forces were not significantly different between ACLRs and controls. CONCLUSION: ACLRs had lower bodyweight-scaled tibiofemoral contact forces during walking, running and sidestepping, likely due to lower knee flexion moments and straighter knee during the different gait tasks. The relative contributions of muscles and external loads to the contact forces were equivalent between groups

Generative deep learning applied to biomechanics: creating an infinite number of realistic walking data for modelling and data augmentation purposes

Our work using generative deep learning models to generate synthetic human movement data to augment existing datasets was presented at the 9th World Congress of Biomechanics