A lower extremity model for muscle-driven simulation of activity using explicit finite element modeling

Computational study of extrinsic factors affecting ACL strain during single-leg jump landing

BACKGROUND

Non-contact anterior cruciate ligament (ACL) injuries are a major concern in sport-related activities due to dynamic knee movements. There is a paucity of finite element (FE) studies that have accurately replicated the knee geometry, kinematics, and muscle forces during dynamic activities. The objective of this study was to develop and validate a knee FE model and use it to quantify the relationships between sagittal plane knee kinematics, kinetics and the resulting ACL strain.

METHODS

3D images of a cadaver knee specimen were segmented (bones, cartilage, and meniscus) and meshed to develop the FE model. Knee ligament insertion sites were defined in the FE model via experimental digitization of the specimen's ligaments. The response of the model was validated against multiple physiological knee movements using published experimental data. Single-leg jump landing motions were then simulated on the validated model with muscle forces and kinematic inputs derived from motion capture and rigid body modelling of ten participants.

RESULTS

The maximum ACL strain measured with the model during jump landing was 3.5 ± 2.2%, comparable to published experimental results. Bivariate analysis showed no significant correlation between body weight, ground reaction force and sagittal plane parameters (such as joint flexion angles, joint moments, muscle forces, and joint velocity) and ACL strain. Multivariate regression analysis showed increasing trunk, hip and ankle flexion angles decreases ACL strain (R2 = 90.04%, p < 0.05).

CONCLUSIONS

Soft landing decreases ACL strain and the relationship could be presented through an empirical equation. The model and the empirical relation developed in this study could be used to better predict ACL injury risk and prevention strategies during dynamic activities.

Load transfer in bone after partial, multi-compartmental, and total knee arthroplasty

Introduction: Arthroplasty-associated bone loss remains a clinical problem: stiff metallic implants disrupt load transfer to bone and, hence, its remodeling stimulus. The aim of this research was to analyze how load transfer to bone is affected by different forms of knee arthroplasty: isolated partial knee arthroplasty (PKA), compartmental arthroplasty [combined partial knee arthroplasty (CPKA), two or more PKAs in the same knee], and total knee arthroplasty (TKA). Methods: An experimentally validated subject-specific finite element model was analyzed native and with medial unicondylar, lateral unicondylar, patellofemoral, bi-unicondylar, medial bicompartmental, lateral bicompartmental, tricompartmental, and total knee arthroplasty. Three load cases were simulated for each: gait, stair ascent, and sit-to-stand. Strain shielding and overstraining were calculated from the differences between the native and implanted states. Results: For gait, the TKA femoral component led to mean strain shielding (30%) more than three times higher than that of PKA (4%-7%) and CPKA (5%-8%). Overstraining was predicted in the proximal tibia (TKA 21%; PKA/CPKA 0%-6%). The variance in the distribution for TKA was an order of magnitude greater than for PKA/CPKA, indicating less physiological load transfer. Only the TKA-implanted femur was sensitive to the load case: for stair ascent and gait, almost the entire distal femur was strain-shielded, whereas during sit-to-stand, the posterior femoral condyles were overstrained. Discussion: TKA requires more bone resection than PKA and CPKA. These finite element analyses suggest that a longer-term benefit for bone is probable as partial and multi-compartmental knee procedures lead to more natural load transfer compared to TKA. High-flexion activity following TKA may be protective of posterior condyle bone resorption, which may help explain why bone loss affects some patients more than others. The male and female bone models used for this research are provided open access to facilitate future research elsewhere.

Two-to-three times increase in natural hip and lumbar non-sagittal plane kinematics can lead to anterior cruciate ligament injury and cartilage failure scenarios during single-leg landings

BACKGROUND

Analyzing sports injuries is essential to mitigate risk for injury, but inherently challenging using in vivo approaches. Computational modeling is a powerful engineering tool used to access biomechanical information on tissue failure that cannot be obtained otherwise using traditional motion capture techniques.

METHODS

We extrapolated high-risk kinematics associated with ACL strain and cartilage load and stress from a previous motion analysis of 14 uninjured participants. Computational simulations were used to induce ACL failure strain and cartilage failure load, stress, and contact pressure in two age- and BMI-matched participants, one of each biological sex, during single-leg cross drop and single-leg drop tasks. The high-risk kinematics were exaggerated in 20% intervals, within their physiological range of motion, to determine if injury occurred in the models. Where injury occurred, we reported the kinematic profiles that led to tissue failure.

FINDINGS

Our findings revealed ACL strains up to 9.99%, consistent with reported failure values in existing literature. Cartilage failure was observed in all eight analyzed conditions when increasing each high-risk kinematic parameter by 2.61 ± 0.67 times the participants' natural landing values. The kinematics associated with tissue failure included peak hip internal rotation of 22.48 ± 19.04°, peak hip abduction of 22.51 ± 9.09°, and peak lumbar rotation away from the stance limb of 11.56 ± 9.78°.

INTERPRETATION

Our results support the ability of previously reported high-risk kinematics in the literature to induce injury and add to the literature by reporting extreme motion limits leading to injurious cases. Therefore, training programs able to modify these motions during single-leg landings may reduce the risk of ACL injury and cartilage trauma.

Simultaneous Evaluation of Tibiofemoral and Patellofemoral Mechanics in Total Knee Arthroplasty: A Combined Experimental and Computational Approach

Contemporary total knee arthroplasty (TKA) has not fully restored natural patellofemoral (P-F) mechanics across the patient population. Previous experimental simulations have been limited in their ability to create dynamic, unconstrained, muscle-driven P-F articulation while simultaneously controlling tibiofemoral (T-F) contact mechanics. The purpose of this study was to develop a novel experimental simulation and validate a corresponding finite element model to evaluate T-F and P-F mechanics. A commercially available wear simulator was retrofitted with custom fixturing to evaluate whole-knee TKA mechanics with varying patella heights during a simulated deep knee bend. A corresponding dynamic finite element model was developed to validate kinematic and kinetic predictions against experimental measurements. Patella alta reduced P-F reaction forces in early and midflexion, corresponding with an increase in T-F forces that indicated an increase in extensor mechanism efficiency. Due to reduced wrapping of the extensor mechanism in deeper flexion for the alta condition, peak P-F forces in flexion increased from 101% to 135% of the applied quadriceps load for the baja and alta conditions, respectively. Strong agreement was observed between the experiment and model predictions with root-mean-square errors (RMSE) for P-F kinematics ranging from 0.8 deg to 3.3 deg and 0.7 mm to 1.4 mm. RMSE for P-F forces ranged from 7.4 N to 53.6 N. By simultaneously controlling dynamic, physiological loading of the T-F and P-F joint, this novel experimental simulation and validated model will be a valuable tool for investigation of future TKA designs and surgical techniques.

Effect of Whole Body Parameters on Knee Joint Biomechanics: Implications for ACL Injury Prevention During Single-Leg Landings

BACKGROUND

Previous studies have examined the effect of whole body (WB) parameters on anterior cruciate ligament (ACL) strain and loads, as well as knee joint kinetics and kinematics. However, articular cartilage damage occurs in relation to ACL failure, and the effect of WB parameters on ACL strain and articular cartilage biomechanics during dynamic tasks is unclear.

PURPOSES

(1) To investigate the effect of WB parameters on ACL strain, as well as articular cartilage stress and contact force, during a single-leg cross drop (SLCD) and single-leg drop (SLD). (2) To identify WB parameters predictive of high ACL strain during these tasks.

STUDY DESIGN

Descriptive laboratory study.

METHODS

Three-dimensional motion analysis data from 14 physically active men and women were recorded during an SLCD and SLD. OpenSim was used to obtain their kinematics, kinetics, and muscle forces for the WB model. Using these data in kinetically driven finite element simulations of the knee joint produced outputs of ACL strains and articular cartilage stresses and contact forces. Spearman correlation coefficients were used to assess relationships between WB parameters and ACL strain and cartilage biomechanics. Moreover, receiver operating characteristic curve analyses and multivariate binary logistic regressions were used to find the WB parameters that could discriminate high from low ACL strain trials.

RESULTS

Correlations showed that more lumbar rotation away from the stance limb at peak ACL strain had the strongest overall association (ρ = 0.877) with peak ACL strain. Higher knee anterior shear force (ρ = 0.895) and lower gluteus maximus muscle force (ρ = 0.89) at peak ACL strain demonstrated the strongest associations with peak articular cartilage stress or contact force in ≥1 of the analyzed tasks. The regression model that used muscle forces to predict high ACL strain trials during the dominant limb SLD yielded the highest accuracy (93.5%), sensitivity (0.881), and specificity (0.952) among all regression models.

CONCLUSION

WB parameters that were most consistently associated with and predictive of high ACL strain and poor articular cartilage biomechanics during the SLCD and SLD tasks included greater knee abduction angle at initial contact and higher anterior shear force at peak ACL strain, as well as lower gracilis, gluteus maximus, and medial gastrocnemius muscle forces.

CLINICAL RELEVANCE

Knowledge of which landing postures create a high risk for ACL or cartilage injury may help reduce injuries in athletes by avoiding those postures and practicing the tasks with reduced high-risk motions, as well as by strengthening the muscles that protect the knee during single-leg landings.

Modeling of active skeletal muscles: a 3D continuum approach incorporating multiple muscle interactions

Skeletal muscles have a highly organized hierarchical structure, whose main function is to generate forces for movement and stability. To understand the complex heterogeneous behaviors of muscles, computational modeling has advanced as a non-invasive approach to evaluate relevant mechanical quantities. Aiming to improve musculoskeletal predictions, this paper presents a framework for modeling 3D deformable muscles that includes continuum constitutive representation, parametric determination, model validation, fiber distribution estimation, and integration of multiple muscles into a system level for joint motion simulation. The passive and active muscle properties were modeled based on the strain energy approach with Hill-type hyperelastic constitutive laws. A parametric study was conducted to validate the model using experimental datasets of passive and active rabbit leg muscles. The active muscle model with calibrated material parameters was then implemented to simulate knee bending during a squat with multiple quadriceps muscles. A computational fluid dynamics (CFD) fiber simulation approach was utilized to estimate the fiber arrangements for each muscle, and a cohesive contact approach was applied to simulate the interactions among muscles. The single muscle simulation results showed that both passive and active muscle elongation responses matched the range of the testing data. The dynamic simulation of knee flexion and extension showed the predictive capability of the model for estimating the active quadriceps responses, which indicates that the presented modeling pipeline is effective and stable for simulating multiple muscle configurations. This work provided an effective framework of a 3D continuum muscle model for complex muscle behavior simulation, which will facilitate additional computational and experimental studies of skeletal muscle mechanics. This study will offer valuable insight into the future development of multiscale neuromuscular models and applications of these models to a wide variety of relevant areas such as biomechanics and clinical research.

Biomechanical effects of medial meniscus radial tears on the knee joint during gait: A concurrent finite element musculoskeletal framework investigation

The biomechanical variation in the knee during walking that accompanies medial meniscal radial tears stemming from knee osteoarthritis (OA) has not been explored. This study introduced a finite element musculoskeletal model using concurrent lower limb musculoskeletal dynamics and knee joint finite element analysis in a single framework and expanded the models to include knees with medial meniscal radial tears and total medial meniscectomy. The radial tears involved three locations: anterior horn, midbody, and posterior horn with grades of 33%, 50%, and 83% of the meniscus width. The shear and hoop stresses of the tear meniscus and tibial cartilage contact load, accompanying tears, and postmeniscectomy were evaluated during the stance phase of the gait cycle using the models. In the 83% width midbody tear group, shear stress at the end of the tear was significantly greater than in the intact meniscus and other tear groups, and the maximum shear stress was increased by 310% compared to the intact meniscus. A medial meniscus radial tear has a much smaller effect on the tibial cartilage load (even though in the 83% width tear, the cartilage/total load ratio increased by only 9%). However, the contact force on the tibial cartilage with total postmeniscectomy was increased by 178.93% compared with a healthy intact meniscus, and the peak contact pressure after meniscectomy increased from 11.94 to 12.45 MPa to 17.64 and 13.76 MPa, at the maximum weight acceptance and push-off, respectively. Our study shows that radial tears with larger medial meniscus widths are prone to high stress concentrations at the end of the tears, leading to the potential risk of complete meniscal rupture. Furthermore, although the tears did not change the cartilage load distribution, they disrupted the circumferential stress-transmitting function of the meniscus, thus greatly increasing the likelihood of the onset of knee OA. The significant increase in the tibial cartilage load with total postmeniscectomy indicates a potential risk of OA flare-ups. This study contributes to a better understanding of meniscal tear-induced OA biomechanical changes during human activities and offers some potential directions for surgical guidance of meniscectomies and the prophylaxis and treatment of OA.

A Computationally Efficient Lower Limb Finite Element Musculoskeletal Framework Directly Driven Solely by Inertial Measurement Unit Sensors

Finite element musculoskeletal (FEMS) approaches using concurrent musculoskeletal and finite element models driven by motion data such as marker-based motion trajectory can provide insight into the interactions between the knee joint secondary kinematics, contact mechanics, and muscle forces in subject-specific biomechanical investigations. However, these data-driven FEMS systems have two major disadvantages that make them challenging to apply in clinical environments: they are computationally expensive and they require expensive and inconvenient equipment for data acquisition. In this study, we developed an FEMS model of the lower limb driven solely by inertial measurement unit sensors that includes the tissue geometries of the entire knee joint and combines muscle modeling and elastic foundation theory-based contact analysis of knee into a single framework. The model requires only the angular velocities and accelerations measured by the sensors as input, and the target outputs (knee contact mechanics, secondary kinematics, and muscle forces) are predicted from the convergence results of iterative calculations of muscle force optimization and knee contact mechanics. To evaluate its accuracy, the model was compared with in vivo experimental data during gait. The maximum contact pressure (12.6 MPa) in the rigid body contact analysis occurred on the medial side of the cartilage at the maximum loading response. The proposed computationally efficient framework drastically reduced the computational time (97.5% reduction) in comparison with the conventional deformable finite element analysis. The developed framework combines measurement convenience and computational efficiency and shows promise for clinical applications.

Apparatus for In Vivo Knee Laxity Assessment Using High-Speed Stereo Radiography

Computational modeling is of growing importance in orthopedics and biomechanics as a tool to understand differences in pathology and predict outcomes from surgical interventions. However, the computational models of the knee have historically relied on in vitro data to create and calibrate model material properties due to the unavailability of accurate in vivo data. This work demonstrates the design and use of a custom device to quantify anterior-posterior (AP) and internal-external (IE) in vivo knee laxity, with an accuracy similar to existing in vitro methods. The device uses high-speed stereo radiography (HSSR) tracking techniques to accurately measure the resulting displacements of the femur, tibia, and patella bones during knee laxity assessment at multiple loads and knee flexion angles. The accuracy of the knee laxity apparatus was determined by comparing laxity data from two cadaveric specimens between the knee laxity apparatus and an existing in vitro robotic knee joint simulator. The accuracy of the knee laxity apparatus was within 1 mm (0.04 in.) for AP and 2.5 deg for IE. Additionally, two living subjects completed knee laxity testing to confirm the laboratory use of the novel apparatus. This work demonstrates the ability to use custom devices in HSSR to collect accurate data, in vivo, for calibration of computational models.

Integration of neural architecture within a finite element framework for improved neuromusculoskeletal modeling

Neuromusculoskeletal (NMS) models can aid in studying the impacts of the nervous and musculoskeletal systems on one another. These computational models facilitate studies investigating mechanisms and treatment of musculoskeletal and neurodegenerative conditions. In this study, we present a predictive NMS model that uses an embedded neural architecture within a finite element (FE) framework to simulate muscle activation. A previously developed neuromuscular model of a motor neuron was embedded into a simple FE musculoskeletal model. Input stimulation profiles from literature were simulated in the FE NMS model to verify effective integration of the software platforms. Motor unit recruitment and rate coding capabilities of the model were evaluated. The integrated model reproduced previously published output muscle forces with an average error of 0.0435 N. The integrated model effectively demonstrated motor unit recruitment and rate coding in the physiological range based upon motor unit discharge rates and muscle force output. The combined capability of a predictive NMS model within a FE framework can aid in improving our understanding of how the nervous and musculoskeletal systems work together. While this study focused on a simple FE application, the framework presented here easily accommodates increased complexity in the neuromuscular model, the FE simulation, or both.

Hamstrings Contraction Regulates the Magnitude and Timing of the Peak ACL Loading During the Drop Vertical Jump in Female Athletes

Background

Anterior cruciate ligament (ACL) injury reduction training has focused on lower body strengthening and landing stabilization. In vitro studies have shown that quadriceps forces increase ACL strain, and hamstring forces decrease ACL strain. However, the magnitude of the effect of the quadriceps and hamstrings forces on ACL loading and its timing during in vivo landings remains unclear.

Purpose

To investigate the effect and timing of knee muscle forces on ACL loading during landing.

Study Design

Descriptive laboratory study.

Methods

A total of 13 young female athletes performed drop vertical jump trials, and their movements were recorded with 3-dimensional motion capture. Lower limb joint motion and muscle forces were estimated with OpenSim and applied to a musculoskeletal finite element (FE) model to estimate ACL loading during landings. The FE simulations were performed with 5 different conditions that included/excluded kinematics, ground-reaction force (GRF), and muscle forces.

Results

Simulation of landing kinematics without GRF or muscle forces yielded an estimated median ACL strain and force of 5.1% and 282.6 N. Addition of GRF to kinematic simulations increased ACL strain and force to 6.8% and 418.4 N (P < .05). Addition of quadriceps force to kinematics + GRF simulations nonsignificantly increased ACL strain and force to 7.2% and 478.5 N. Addition of hamstrings force to kinematics + GRF simulations decreased ACL strain and force to 2.6% and 171.4 N (P < .001). Addition of all muscles to kinematics + GRF simulations decreased ACL strain and force to 3.3% and 195.1 N (P < .001). With hamstrings force, ACL loading decreased from initial contact (time of peak: 1-18 milliseconds) while ACL loading without hamstrings force peaked at 47 to 98 milliseconds after initial contact (P = .024-.001). The knee flexion angle increased from 20.9° to 73.1° within 100 milliseconds after initial contact.

Conclusion

Hamstrings activation had greater effect relative to GRF and quadriceps activation on ACL loading, which significantly decreased and regulated the magnitude and timing of ACL loading during in vivo landings.

Clinical Relevance

Clinical training should focus on strategies that influence increased hamstrings activation during landing to reduce ACL loads.

Using Musculoskeletal Models to Estimate in vivo Total Knee Replacement Kinematics and Loads: Effect of Differences Between Models

Total knee replacement (TKR) is one of the most performed orthopedic surgeries to treat knee joint diseases in the elderly population. Although the survivorship of knee implants may extend beyond two decades, the poor outcome rate remains considerable. A recent computational approach used to better understand failure modes and improve TKR outcomes is based on the combination of musculoskeletal (MSK) and finite element models. This combined multiscale modeling approach is a promising strategy in the field of computational biomechanics; however, some critical aspects need to be investigated. In particular, the identification and quantification of the uncertainties related to the boundary conditions used as inputs to the finite element model due to a different definition of the MSK model are crucial. Therefore, the aim of this study is to investigate this problem, which is relevant for the model credibility assessment process. Three different generic MSK models available in the OpenSim platform were used to simulate gait, based on the experimental data from the fifth edition of the "Grand Challenge Competitions to Predict in vivo Knee Loads." The outputs of the MSK analyses were compared in terms of relative kinematics of the knee implant components and joint reaction (JR) forces and moments acting on the tibial insert. Additionally, the estimated knee JRs were compared with those measured by the instrumented knee implant so that the "global goodness of fit" was quantified for each model. Our results indicated that the different kinematic definitions of the knee joint and the muscle model implemented in the different MSK models influenced both the motion and the load history of the artificial joint. This study demonstrates the importance of examining the influence of the model assumptions on the output results and represents the first step for future studies that will investigate how the uncertainties in the MSK models propagate on disease-specific finite element model results.

Embodiment of intra-abdominal pressure in a flexible multibody model of the trunk and the spinal unloading effects during static lifting tasks

The role of intra-abdominal pressure (IAP) in spinal load reduction has remained controversial, partly because previous musculoskeletal models did not introduce the pressure generating mechanism. In this study, an integrated computational methodology is proposed to combine the IAP change with core muscle activations. An ideal gas relationship was introduced to calculate pressure distribution within the abdominal cavity. Additionally, based on flexible multibody dynamics, a muscle membrane element was developed by incorporating the muscular fiber deformation, inter-fiber stiffness, and volume constancy. This element was then utilized in discretizing the diaphragm and transversus abdominis, forming an IAP-muscle coupling system of the abdominal cavity. Based on this methodology, a forward dynamic simulation of spinal flexion was presented to examine the unloading effect of abdominal breathing. The results confirm that core muscle contraction during the abdominal breathing cycle can substantially reduce the forces of spinal compression together with trunk extensor muscles, and this effect is more pronounced when the IAP increase is produced by contraction of the transversus abdominis. This unloading effect still holds even with the co-activation of other abdominal muscles, providing a potential choice when designing trunk movements during weight-lifting tasks.

Machine learning for rapid estimation of lower extremity muscle and joint loading during activities of daily living

Joint contact and muscle forces estimated with musculoskeletal modeling techniques offer useful metrics describing movement quality that benefit multiple research and clinical applications. The expensive processing of laboratory data associated with generating these outputs presents challenges to researchers and clinicians, including significant time and expertise requirements that limit the number of subjects typically evaluated. The objective of the current study was to develop and compare machine learning techniques for rapid, data-driven estimation of musculoskeletal metrics from derived gait lab data. OpenSim estimates of patient joint and muscle forces during activities of daily living were simulated using laboratory data from 70 total knee replacement patients and used to develop 4 different machine learning algorithms. Trained machine learning models predicted both trend and magnitude of estimated joint contact (mean correlation coefficients ranging from 0.93 to 0.94 during gait) and muscle forces (mean correlation coefficients ranging from 0.83 to 0.91 during gait) based on anthropometrics, ground reaction forces, and joint angle data. Patient mechanics were accurately predicted by recurrent neural networks, even after removing dependence on key subsets of predictor features. The ability to quickly estimate patient mechanics from derived measurements of movement has the potential to broaden the impact of musculoskeletal modeling by enabling faster assessment in both clinical and research settings.

Anterior Cruciate Ligament Loading Increases With Pivot-Shift Mechanism During Asymmetrical Drop Vertical Jump in Female Athletes

Background:

Frontal plane trunk lean with a side-to-side difference in lower extremity kinematics during landing increases unilateral knee abduction moment and consequently anterior cruciate ligament (ACL) injury risk. However, the biomechanical features of landing with higher ACL loading are still unknown. Validated musculoskeletal modeling offers the potential to quantify ACL strain and force during a landing task.

Purpose:

To investigate ACL loading during a landing and assess the association between ACL loading and biomechanical factors of individual landing strategies.

Study Design:

Descriptive laboratory study.

Methods:

Thirteen young female athletes performed drop vertical jump trials, and their movements were recorded with 3-dimensional motion capture. Electromyography-informed optimization was performed to estimate lower limb muscle forces with an OpenSim musculoskeletal model. A whole-body musculoskeletal finite element model was developed. The joint motion and muscle forces obtained from the OpenSim simulations were applied to the musculoskeletal finite element model to estimate ACL loading during participants’ simulated landings with physiologic knee mechanics. Kinematic, muscle force, and ground-reaction force waveforms associated with high ACL strain trials were reconstructed via principal component analysis and logistic regression analysis, which were used to predict trials with high ACL strain.

Results:

The median (interquartile range) values of peak ACL strain and force during the drop vertical jump were 3.3% (–1.9% to 5.1%) and 195.1 N (53.9 to 336.9 N), respectively. Four principal components significantly predicted high ACL strain trials, with 100% sensitivity, 78% specificity, and an area of 0.91 under the receiver operating characteristic curve (P < .001). High ACL strain trials were associated with (1) knee motions that included larger knee abduction, internal tibial rotation, and anterior tibial translation and (2) motion that included greater vertical and lateral ground-reaction forces, lower gluteus medius force, larger lateral pelvic tilt, and increased hip adduction.

Conclusion:

ACL loads were higher with a pivot-shift mechanism during a simulated landing with asymmetry in the frontal plane. Specifically, knee abduction can create compression on the posterior slope of the lateral tibial plateau, which induces anterior tibial translation and internal tibial rotation.

Clinical Relevance:

Athletes are encouraged to perform interventional and preventive training to improve symmetry during landing.

ReadySim: A computational framework for building explicit finite element musculoskeletal simulations directly from motion laboratory data

Musculoskeletal modeling allows researchers insight into joint mechanics which might not otherwise be obtainable through in vivo or in vitro studies. Common musculoskeletal modeling techniques involve rigid body dynamics software which often employ simplified joint representations. These representations have proven useful but are limited in performing single-framework deformable analyzes in structures of interest. Musculoskeletal finite element (MSFE) analysis allows for representation of structures in sufficient detail to obtain accurate solutions of the internal stresses and strains including complex contact conditions and material representations. Studies which performed muscle force optimization directly in a finite element framework were often limited in complexity to minimize computational time. Recent advances in computational efficiency and control schemes for muscle force prediction have made these solutions more practical. Yet, the formulation of subject-specific simulations remains a challenging problem. The objectives of this work were to develop an open-source computational framework to build and run simulations which (a) scale the size of MSFE models and efficiently estimate (b) joint kinematics and (c) muscle forces from human motion data collected in a typical gait laboratory. A computational framework was built using MATLAB and Python to interface with model input and output files. The software uses laboratory marker data to scale model segment lengths and estimate joint kinematics. Concurrent muscle force and tissue strain estimations are performed based on the estimated kinematics and ground reaction forces. This software will improve the usability and consistency of single-framework MSFE simulations. Both software and template model are made freely available on SimTK.Novelty Statement Single framework musculoskeletal modeling directly in a finite element environment for muscle force estimation and tissue strain analysis. Open dissemination of unilateral musculoskeletal finite element model and software used in manuscript.

Development and validation of a finite-element musculoskeletal model incorporating a deformable contact model of the hip joint during gait

Musculoskeletal models provide non-invasive and subject-specific biomechanical investigations of the musculoskeletal system. In a musculoskeletal model, muscle forces contribute to the deformation and kinematics of the joint which in turn would alter moment arms of muscles and ground reaction forces and thus affect the prediction of muscle forces and contact forces and contact mechanics of the joint. By far, deformable contact models of the hip have not been considered in musculoskeletal models, and the role of kinematics and deformation within the hip in muscle forces and hip contact mechanics is unknown. In this study, an FE musculoskeletal model including bones, joints and muscles of the lower extremity was developed. A deformable contact model of the hip joint was incorporated and coupled into the musculoskeletal model. Joint angles and ground reaction forces during gait were used as inputs. Optimization minimizing the sum of muscle stresses squared was performed directly to the FE musculoskeletal model in order to simultaneously solve muscle forces and contact forces and contact stresses of the hip joint within a single framework. The calculated hip contact forces corresponded well to the in vivo measurement data. The maximum hip contact stress was 6.5 MPa and occurred at weight-acceptance. The influence of kinematics and deformation in the hip on muscles forces and hip contact forces was minimal and not sensitive to variations in the thickness and properties of the joint cartilage during gait. This suggests that the uncoupled approach in which the hip contact forces and contact mechanics are simulated in separate frameworks would serve as an effective and efficient alternative for subject-specific modelling of the hip. This study provides guidance for the level of complexity needed for future hip models and can be used to evaluate biomechanical changes of the musculoskeletal system following interventions.

Semi-supervised learning for automatic segmentation of the knee from MRI with convolutional neural networks

BACKGROUND AND OBJECTIVE

Segmentation is a crucial step in multiple biomechanics and orthopedics applications. The time-intensiveness and expertise requirements of medical image segmentation present a significant bottleneck for corresponding workflows. The current study develops and evaluates convolutional neural networks (CNNs) for automatic segmentation of magnetic resonance imaging (MRI) with the objective of assessing their utility for use in biomechanics research methods.

METHODS

CNNs were developed using a previously published, fully-annotated dataset as well as unlabeled scans from a publicly-available dataset. 2D and 3D CNNs were trained using semi-supervised learning frameworks for automatic segmentation of six structures of the knee. An inference strategy called Monte Carlo patch sampling was introduced to increase accuracy of the resulting models while adding no additional steps to the training process. Performance was assessed using traditional segmentation metrics, as well as surface error between reconstructed geometries from predicted and manual segmentations. Geometries from predicted segmentation maps were developed into finite element (FE) models in a semi-automatic pipeline and evaluated for FE-readiness.

RESULTS

3D CNNs using Monte Carlo patch sampling during inference achieved an Intersection-over-Union (IoU) of 0.978 and a dice similarity coefficient (DSC) of 0.989. Median surface error between predicted and ground truth geometries ranged from 0.56 to 0.98 mm. Meshes generated from the predicted segmentation maps were successfully used in FE simulations, demonstrating FE-readiness of geometries predicted by CNNs. CNNs trained with semi-supervised techniques outperformed CNNs trained in a fully-supervised fashion and resulted in performance competitive with similar literature despite relying on significantly less labeled data.

CONCLUSIONS

CNNs developed for automatic segmentation have potential for supplementing manual segmentation workflows in a wide range of orthopedics and biomechanics applications, including FE analysis. Faster processing times for developing FE models can enable population-based FE analysis using subject-specific models. The use of semi-supervised learning algorithms may additionally help circumvent the cost of obtaining labeled data in the development of these models.

EFFECTS OF SINGLE CROUCH WALKING GAITS ON FATIGUE DAMAGES OF LOWER EXTREMITY MAIN MUSCLES

Single crouch walking gait (SCWG) is one of the main walking gaits when one of the legs is injured. However, the research on movement biomechanical characteristics (MBC) of lower extremity in SCWG has not been reported. So, the aim of this study is to analyze kinematics and main muscle fatigue damages of lower extremity in SCWG. Gaits data were collected with functional assessment biomechanics (FAB) system which applies the real-time wireless gait phase detection system, the movement function relations of joints of lower extremity were obtained by fitting the collected data in normal walking gait (NWG) and SCWG with Fourier series, the fitted movement functions were input to 3D human musculoskeletal model as driving functions for human movement to analyze the differences of MBC between SCWG and NWG. Finally, the muscle contractions were used to evaluate muscle fatigue damage. Compared with NWG, the result shows that the movement range of the joint angles of lower extremity are reduced in SCWG, the change law of hip internal/external rotation angle (IERA) has a significant difference, and the change laws of other joint angles are similarly between SCWG and NWG. The muscle contractions of Gluteus maximum (GMAX) 2, Gluteus meddle (GMED) 1, GMED2, Iliacus (ILI), Rectus femoris (RFEM), Soleus (SOL), Gastrocnemius (GAS) and Vastus lateralis (VLAT) are significantly larger in SCWG than in NWG (except GlutMed2 which is about 20–40% and VLAT which is about 63.8–76.1% of gait cycle), namely, these muscles easily cause muscle damages in SCWG. The contraction change of Adductor magnus (AMAG) is dispersed, so AMAG is prone to muscle fatigue in SCWG. The study results will fill in the gaps in the MBC of lower extremity in SCWG and provide data for Rehabilitation Medical Technology (RMT) and development of rehabilitation equipment of lower extremity.

PManalyzer: A Software Facilitating the Study of Sensorimotor Control of Whole-Body Movements

Motion analysis is used to study the functionality or dysfunctionality of the neuromuscular system, as human movements are the direct outcome of neuromuscular control. However, motion analysis often relies on measures that quantify simplified aspects of a motion, such as specific joint angles, despite the well-known complexity of segment interactions. In contrast, analyzing whole-body movement patterns may offer a new understanding of movement coordination and movement performance. Clinical research and sports technique evaluations suggest that principal component analysis (PCA) provides novel and valuable insights into control aspects of the neuromuscular system and how they relate to coordinative patterns. However, the implementation of PCA computations are time consuming, and require mathematical knowledge and programming skills, drastically limiting its application in current research. Therefore, the aim of this study is to present the Matlab software tool "PManalyzer" to facilitate and encourage the application of state-of-the-art PCA concepts in human movement science. The generalized PCA concepts implemented in the PManalyzer allow users to apply a variety of marker set independent PCA-variables on any kinematic data and to visualize the results with customizable plots. In addition, the extracted movement patterns can be explored with video options that may help testing hypotheses related to the interplay of segments. Furthermore, the software can be easily modified and adapted to any specific application.