Development and validation of subject-specific pediatric multibody knee kinematic models with ligamentous constraints

Prediction of In Vivo Knee Mechanics During Daily Activities Based on a Musculoskeletal Model Incorporated with a Subject-Specific Knee Joint

The objective of this study was to develop a musculoskeletal model incorporated with a subject-specific knee joint to predict the tibiofemoral contact force (TFCF) during daily motions. For this purpose, 18 healthy participants were recruited to perform the motion data acquisition using synchronized motion capture and force platform systems, and motion simulation based on an improved musculoskeletal model for five daily activities, including normal walking, stair ascent, stair descent, sit-to-stand, and stand-to-sit. The proposed musculoskeletal model included subject-specific models of bones, cartilages, and meniscus, detailed knee ligaments and muscles, deformable elastic contacts, and multiple degrees of freedom (DOFs) of the knee joint. The prediction accuracy was demonstrated by the good agreements of TFCF curves between the model predictions and in vivo measurements for the five activities (RMSE: 0.216~0.311 BW, R2: 0.928~0.992, and CE: 0.048~0.141). Based on the validated model, the TFCF on total, medial, and lateral compartments (TFCFTotal, TFCFMedial, and TFCFLateral) during the five daily activities were predicted. For TFCFTotal, the peak force for stair descent or sit-to-stand was the largest, followed by stair ascent or stand-to-sit, and finally normal walking. For TFCFMedial, stair descent had the largest peak, followed by stair ascent. There were no significant differences between the peak TFCFMedial values of normal walking, sit-to-stand, and stand-to-sit. For TFCFLateral, the peak of sit-to-stand was the largest, followed by stand-to-sit or stair descent, and finally normal walking or stair ascent. This study is valuable for further understanding the biomechanics of a healthy knee joint and providing theoretical guidance for the treatment of knee osteoarthritis (KOA).

Enhancing biomechanical outcomes in proximal femoral osteotomy through optimised blade plate sizing: A neuromusculoskeletal-informed finite element analysis

Proximal femoral osteotomy (PFO) is a frequently performed surgical procedure to correct hip deformities in the paediatric population. The optimal size of the blade plate implant in PFO is a critical but underexplored factor influencing biomechanical outcomes. This study introduces a novel approach to refine implant selection by integrating personalized neuromusculoskeletal modelling with finite element analysis. Using computed tomography scans and walking gait data from six paediatric patients with various pathologies and deformities, we assessed the impact of four distinct implant width-to-femoral neck diameter (W-D) ratios (30 %, 40 %, 50 %, and 60 %) on surgical outcomes. The results show that the risk of implant yield generally decreases with increasing W-D ratio, except for Patient P2, where the yield risk remained below 100 % across all ratios. The implant factor of safety (FoS) increased with larger W-D ratios, except for Patients P2 and P6, where the highest FoS was 2.60 (P2) and 0.49 (P6) at a 60 % W-D ratio. Bone-implant micromotion consistently remained below 40 µm at higher W-D ratios, with a 50 % W-D ratio striking the optimal balance for mechanical stability in all patients except P6. Although interfragmentary and principal femoral strains did not display consistent trends across all patients, they highlight the need for patient-specific approaches to ensure effective fracture healing. These findings highlight the importance of patient-specific considerations in implant selection, offering surgeons a more informed pathway to enhance patient outcomes and extend implant longevity. Additionally, the insights gained from this study provide valuable guidance for manufacturers in designing next-generation blade plates tailored to improve biomechanical performance in paediatric orthopaedics.

Automatic generation of knee kinematic models from medical imaging

BACKGROUND AND OBJECTIVE

Three-dimensional spatial mechanisms have been used to accurately predict passive knee kinematics, and have shown potential to be used in optimized multibody kinematic models. Such multi-body models are anatomically consistent and can accurately predict passive knee kinematics, but require extensive medical image processing and thus are not widely adopted. This study aimed to automate the generation of kinematic models of tibiofemoral (TFJ) and patellofemoral (PFJ) joints from segmented magnetic resonance imaging (MRI) and compare them against a corresponding manual pipeline.

METHODS

From segmented MRI of eight healthy participants (four females; aged 14.0 ± 2.6 years), geometric parameters (i.e., articular surfaces, ligament attachments) were determined both automatically and manually, and then assembled into TFJ and PFJ kinematic models to predict passive kinematics. The TFJ model was a six-link mechanism with deformable ligamentous constraints, whereas PFJ was a modified hinge. The ligament length changes through TFJ flexion were prescribed to literature strain profile. The geometric parameters were optimized to ensure physiological kinematic predictions through a Multiple Objective Particle Swarm Optimization.

RESULTS

Geometric parameters showed strong agreement between automatic and manual pipelines (median error of 2.8 mm for anatomical landmarks and 1.5 mm for ligament lengths). Predicted TFJ and PFJ kinematics from the two pipelines were not statistically different, except for tibial superior/inferior translation near terminal TFJ extension. The TFJ kinematics predicted from the automatic pipeline had mean errors of 3.6° and 12.4° for adduction/abduction and internal/external rotation, respectively, and <7 mm mean translational error compared to the manual pipeline. Predicted PFJ had <9° mean rotational errors and <6 mm mean translational errors.

CONCLUSIONS

The automatic pipeline developed and presented here can predict passive knee kinematics comparable to a manual pipeline, but removes laborious manual processing and provides a systematic approach to model creation.

The future of in-field sports biomechanics: wearables plus modelling compute real-time in vivo tissue loading to prevent and repair musculoskeletal injuries

This paper explores the use of biomechanics in identifying the mechanistic causes of musculoskeletal tissue injury and degeneration. It appraises how biomechanics has been used to develop training programmes aiming to maintain or recover tissue health. Tissue health depends on the functional mechanical environment experienced by tissues during daily and rehabilitation activities. These environments are the result of the interactions between tissue motion, loading, biology, and morphology. Maintaining health of and/or repairing musculoskeletal tissues requires targeting the "ideal" in vivo tissue mechanics (i.e., loading and deformation), which may be enabled by appropriate real-time biofeedback. Recent research shows that biofeedback technologies may increase their quality and effectiveness by integrating a personalised neuromusculoskeletal modelling driven by real-time motion capture and medical imaging. Model personalisation is crucial in obtaining physically and physiologically valid predictions of tissue biomechanics. Model real-time execution is crucial and achieved by code optimisation and artificial intelligence methods. Furthermore, recent work has also shown that laboratory-based motion capture biomechanical measurements and modelling can be performed outside the laboratory with wearable sensors and artificial intelligence. The next stage is to combine these technologies into well-designed easy to use products to guide training to maintain or recover tissue health in the real-world.

Evaluation of 3D Footprint Morphology of Knee-Related Muscle Attachments Based on CT Data Reconstruction: A Feasibility Study

A three-dimensional (3D) understanding of muscle attachment footprints became increasingly relevant for musculoskeletal modeling. The established method to project attachments as points ignores patient-specific individuality. Research focuses on investigating certain muscle groups rather than comprehensively studying all muscles spanning a joint. Therefore, we present a reliable method to study several muscle attachments in order to reconstruct the attachment sites in 3D based on CT imaging for future applications in musculoskeletal modeling. For the present feasibility study, 23 knee-related muscle attachments were CT-scanned postmortem from four nonadipose male specimens. For this, the specific muscle attachments were dissected and marked with a barium sulfate containing paint (60 g BaSO4 in 30 mL water and 10 mL acrylic paint). Subsequently, bone geometries and muscle attachments were reconstructed and evaluated from CT datasets. Bone morphology and footprint variations were studied. Exemplarily, variations were high for pes anserinus insertions (mean 56%) and the origins of M. biceps femoris (mean 54%). In contrast, the origins of the vastus muscles as well as the insertion of the Achilles tendon showed low variation (mean 9% and 13%, respectively). Most attachment sites showed variation exceeding the individuality of bone morphology. In summary, the present data were consistent with the few published studies of specific muscle footprints. Our data shed light on the high variability of muscle attachments, which need to be addressed when studying muscle forces and movements through musculoskeletal modeling. This is the first step to achieving a more profound understanding of muscle morphology to be utilized in numerical simulations.

Validation and evaluation of subject-specific finite element models of the pediatric knee

Finite element (FE) models have been widely used to investigate knee joint biomechanics. Most of these models have been developed to study adult knees, neglecting pediatric populations. In this study, an atlas-based approach was employed to develop subject-specific FE models of the knee for eight typically developing pediatric individuals. Initially, validation simulations were performed at four passive tibiofemoral joint (TFJ) flexion angles, and the resulting TFJ and patellofemoral joint (PFJ) kinematics were compared to corresponding patient-matched measurements derived from magnetic resonance imaging (MRI). A neuromusculoskeletal-(NMSK)-FE pipeline was then used to simulate knee biomechanics during stance phase of walking gait for each participant to evaluate model simulation of a common motor task. Validation simulations demonstrated minimal error and strong correlations between FE-predicted and MRI-measured TFJ and PFJ kinematics (ensemble average of root mean square errors < 5 mm for translations and < 4.1° for rotations). The FE-predicted kinematics were strongly correlated with published reports (ensemble average of Pearson's correlation coefficients (ρ) > 0.9 for translations and ρ > 0.8 for rotations), except for TFJ mediolateral translation and abduction/adduction rotation. For walking gait, NMSK-FE model-predicted knee kinematics, contact areas, and contact pressures were consistent with experimental reports from literature. The strong agreement between model predictions and experimental reports underscores the capability of sequentially linked NMSK-FE models to accurately predict pediatric knee kinematics, as well as complex contact pressure distributions across the TFJ articulations. These models hold promise as effective tools for parametric analyses, population-based clinical studies, and enhancing our understanding of various pediatric knee injury mechanisms. They also support intervention design and prediction of surgical outcomes in pediatric populations.

Comparative validation of two patient-specific modelling pipelines for predicting knee joint forces during level walking

Over the past few years, the use of computer models and simulations tailored to the patient's physiology to assist clinical decision-making has increased enormously.While several pipelines to develop personalized models exist, their adoption on a large scale is still limited due to the required niche computational skillset and the lengthy operations required. Novel toolboxes, such as STAPLE, promise to streamline and expedite the development of image-based skeletal lower limb models. STAPLE-generated models can be rapidly generated, with minimal user input, and present similar joint kinematics and kinetics compared to models developed employing the established INSIGNEO pipeline. Yet, it is unclear how much the observed discrepancies scale up and affect joint contact force predictions. In this study, we compared image-based musculoskeletal models developed (i) with the INSIGNEO pipeline and (ii) with a semi-automated pipeline that combines STAPLE and nmsBuilder, and assessed their accuracy against experimental implant data.Our results showed that both pipelines predicted similar total knee joint contact forces between one another in terms of profiles and average values, characterized by a moderately high level of agreement with the experimental data. Nonetheless, the Student t-test revealed statistically significant differences between both pipelines. Of note, the STAPLE-based pipeline required considerably less time than the INSIGNEO pipeline to generate a musculoskeletal model (i.e., 60 vs 160 min). This is likely to open up opportunities for the use of personalized musculoskeletal models in clinical practice, where time is of the essence.

The History and Future of Neuromusculoskeletal Biomechanics

The Executive Council of the International Society of Biomechanics has initiated and overseen the commemorations of the Society's 50th Anniversary in 2023. This included multiple series of lectures at the ninth World Congress of Biomechanics in 2022 and XXIXth Congress of the International Society of Biomechanics in 2023, all linked to special issues of International Society of Biomechanics' affiliated journals. This special issue of the Journal of Applied Biomechanics is dedicated to the biomechanics of the neuromusculoskeletal system. The reader is encouraged to explore this special issue which comprises 6 papers exploring the current state-of the-art, and future directions and roles for neuromusculoskeletal biomechanics. This editorial presents a very brief history of the science of the neuromusculoskeletal system's 4 main components: the central nervous system, musculotendon units, the musculoskeletal system, and joints, and how they biomechanically integrate to enable an understanding of the generation and control of human movement. This also entails a quick exploration of contemporary neuromusculoskeletal biomechanics and its future with new fields of application.

Can a Musculoskeletal Model Adapted to Knee Implant Geometry Improve Prediction of 3D Contact Forces and Moments?

Tibiofemoral contact loads are crucial parameters in the onset and progression of osteoarthrosis. While contact loads are frequently estimated from musculoskeletal models, their customization is often limited to scaling musculoskeletal geometry or adapting muscle lines. Moreover, studies have usually focused on superior-inferior contact force without investigating three-dimensional contact loads. Using experimental data from six patients with instrumented total knee arthroplasty (TKA), this study customized a lower limb musculoskeletal model to consider the positioning and the geometry of the implant at knee level. Static optimization was performed to estimate tibiofemoral contact forces and contact moments as well as musculotendinous forces. Predictions from both a generic and a customized model were compared to the instrumented implant measurements. Both models accurately predict superior-inferior (SI) force and abduction-adduction (AA) moment. Notably, the customization improves prediction of medial-lateral (ML) force and flexion-extension (FE) moments. However, there is subject-dependent variability in the prediction of anterior-posterior (AP) force. The customized models presented here predict loads on all joint axes and in most cases improve prediction. Unexpectedly, this improvement was more limited for patients with more rotated implants, suggesting a need for further model adaptations such as muscle wrapping or redefinition of hip and ankle joint centers and axes.

Maintaining soldier musculoskeletal health using personalised digital humans, wearables and/or computer vision

OBJECTIVES

The physical demands of military service place soldiers at risk of musculoskeletal injuries and are major concerns for military capability. This paper outlines the development new training technologies to prevent and manage these injuries.

DESIGN

Narrative review.

METHODS

Technologies suitable for integration into next-generation training devices were examined. We considered the capability of technologies to target tissue level mechanics, provide appropriate real-time feedback, and their useability in-the-field.

RESULTS

Musculoskeletal tissues' health depends on their functional mechanical environment experienced in military activities, training and rehabilitation. These environments result from the interactions between tissue motion, loading, biology, and morphology. Maintaining health of and/or repairing joint tissues requires targeting the "ideal" in vivo tissue mechanics (i.e., loading and strain), which may be enabled by real-time biofeedback. Recent research has shown that these biofeedback technologies are possible by integrating a patient's personalised digital twin and wireless wearable devices. Personalised digital twins are personalised neuromusculoskeletal rigid body and finite element models that work in real-time by code optimisation and artificial intelligence. Model personalisation is crucial in obtaining physically and physiologically valid predictions.

CONCLUSIONS

Recent work has shown that laboratory-quality biomechanical measurements and modelling can be performed outside the laboratory with a small number of wearable sensors or computer vision methods. The next stage is to combine these technologies into well-designed easy to use products.

Development of predictive statistical shape models for paediatric lower limb bones

BACKGROUND AND OBJECTIVE

Accurate representation of bone shape is important for subject-specific musculoskeletal models as it may influence modelling of joint kinematics, kinetics, and muscle dynamics. Statistical shape modelling is a method to estimate bone shape from minimal information, such as anatomical landmarks, and to avoid the time and cost associated with reconstructing bone shapes from comprehensive medical imaging. Statistical shape models (SSM) of lower limb bones have been developed and validated for adult populations but are not applicable to paediatric populations. This study aimed to develop SSM for paediatric lower limb bones and evaluate their reconstruction accuracy using sparse anatomical landmarks.

METHODS

We created three-dimensional models of 56 femurs, 29 pelves, 56 tibias, 56 fibulas, and 56 patellae through segmentation of magnetic resonance images taken from 29 typically developing children (15 females; 13 ± 3.5 years). The SSM for femur, pelvis, tibia, fibula, patella, haunch (i.e., combined femur and pelvis), and shank (i.e., combined tibia and fibula) were generated from manual segmentation of comprehensive magnetic resonance images to describe the shape variance of the cohort. We implemented a leave-one-out cross-validation method wherein SSM were used to reconstruct novel bones (i.e., those not included in SSM generation) using full- (i.e., full segmentation) and sparse- (i.e., anatomical landmarks) input, and then compared these reconstructions against bones segmented from magnetic resonance imaging. Reconstruction performance was evaluated using root mean squared errors (RMSE, mm), Jaccard index (0-1), Dice similarity coefficient (DSC) (0-1), and Hausdorff distance (mm). All results reported in this abstract are mean ± standard deviation.

RESULTS

Femurs, pelves, tibias, fibulas, and patellae reconstructed via SSM using full-input had RMSE between 0.89 ± 0.10 mm (patella) and 1.98 ± 0.38 mm (pelvis), Jaccard indices between 0.77 ± 0.03 (pelvis) and 0.90 ± 0.02 (tibia), DSC between 0.87 ± 0.02 (pelvis) and 0.95 ± 0.01 (tibia), and Hausdorff distances between 2.45 ± 0.57 mm (patella) and 9.01 ± 2.36 mm (pelvis). Reconstruction using sparse-input had RMSE ranging from 1.33 ± 0.61 mm (patella) to 3.60 ± 1.05 mm (pelvis), Jaccard indices ranging from 0.59 ± 0.10 (pelvis) to 0.83 ± 0.03 (tibia), DSC ranging from 0.74 ± 0.08 (pelvis) to 0.90 ± 0.02 (tibia), and Hausdorff distances ranging from 3.21 ± 1.19 mm (patella) to 12.85 ± 3.24 mm (pelvis).

CONCLUSIONS

The SSM of paediatric lower limb bones showed reconstruction accuracy consistent with previously developed SSM and outperformed adult-based SSM when used to reconstruct paediatric bones.

Comparison of kinematic parameters of children gait obtained by inverse and direct models

The purpose of this study is to compare differences between kinematic parameters of pediatric gait obtained by direct kinematics (DK) (Plug-in-Gait) and inverse kinematics (IK) (AnyBody) models. Seventeen healthy children participated in this study. Both lower extremities were examined using a Vicon 8-camera motion capture system and a force plate. Angles of the hip, knee, and ankle joints were obtained based on DK and IK models, and ranges of motion (ROMs) were identified from them. The standard error of measurement, root-mean-squared error, correlation r, and magnitude-phase (MP) metrics were calculated to compare differences between the models’ outcomes. The determined standard error of measurement between ROMs from the DK and IK models ranged from 0.34° to 0.58°. A significant difference was found in the ROMs with the exception of the left hip’s internal/external rotation. The mean RMSE of all joints’ amplitudes exceeded the clinical significance limit and was 13.6 ± 4.0°. The best curve angles matching nature were found in the sagittal plane, where r was 0.79 to 0.83 and MP metrics were 0.05 to 0.30. The kinematic parameters of pediatric gait obtained by IK and DK differ significantly. Preferably, all of the results obtained by DK must be validated/verified by IK, in order to achieve a more accurate functional assessment of the individual. Furthermore, the use of IK expands the capabilities of gait analysis and allows for kinetic characterisation.

The effect of modelling parameters in the development and validation of knee joint models on ligament mechanics: A systematic review

BACKGROUND

The ligaments in the knee are prone to injury especially during dynamic activities. The resulting instability can have a profound impact on a patient's daily activities and functional capacity. Musculoskeletal knee modelling provides a non-invasive tool for investigating ligament force-strain behaviour in various dynamic scenarios, as well as potentially complementing existing pre-planning tools to optimise surgical reconstructions. However, despite the development and validation of many musculoskeletal knee models, the effect of modelling parameters on ligament mechanics has not yet been systematically reviewed.

OBJECTIVES

This systematic review aimed to investigate the results of the most recent studies using musculoskeletal modelling techniques to create models of the native knee joint, focusing on ligament mechanics and modelling parameters in various simulated movements.

DATA SOURCES

PubMed, ScienceDirect, Google Scholar, and IEEE Xplore.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

Databases were searched for articles containing any numerical ligament strain or force data on the intact, ACL-deficient, PCL-deficient, or lateral extra-articular reconstructed (LER) knee joints. The studies had to derive these results from musculoskeletal modelling methods. The dates of the publications were between 1 January 1995 and 30 November 2021.

METHOD

A customised data extraction form was created to extract each selected study's critical musculoskeletal model development parameters. Specific parameters of the musculoskeletal knee model development used in each eligible study were independently extracted, including the (1) musculoskeletal model definition (i.e., software used for modelling, knee type, source of geometry, the inclusion of cartilage and menisci, and articulating joints and joint boundary conditions (i.e., number of degrees of freedom (DoF), subjects, type of activity, collected data and type of simulation)), (2) specifically ligaments modelling techniques (i.e., ligament bundles, attachment points, pathway, wrapping surfaces and ligament material properties such as stiffness and reference length), (3) sensitivity analysis, (4) validation approaches, (5) predicted ligament mechanics (i.e., force, length or strain) and (6) clinical applications if available. The eligible papers were then discussed quantitatively and qualitatively with respect to the above parameters.

RESULTS AND DISCUSSION

From the 1004 articles retrieved by the initial electronic search, only 25 met all inclusion criteria. The results obtained by aggregating data reported in the eligible studies indicate that considerable variability in the predicted ligament mechanics is caused by differences in geometry, boundary conditions and ligament modelling parameters.

CONCLUSION

This systematic review revealed that there is currently a lack of consensus on knee ligament mechanics. Despite this lack of consensus, some papers highlight the potential of developing translational tools using musculoskeletal modelling. Greater consistency in model design, incorporation of sensitivity assessment of the model outcomes and more rigorous validation methods should lead to better agreement in predictions for ligament mechanics between studies. The resulting confidence in the musculoskeletal model outputs may lead to the development of clinical tools that could be used for patient-specific treatments.

A Novel Kinematic Model of the Tibiofemoral Joint Based on a Parallel Mechanism

This paper presents a complete kinematic model of the tibiofemoral joint (TFJ) based on a RRPP + 4-SPS parallel mechanism, where R, P, and S stand for revolute, prismatic, and spherical joints, respectively. The model accounts for the contact between tibia and femur, and the four major ligaments: anterior cruciate, posterior cruciate, medial collateral, and lateral collateral, with anatomical significance in their length variations. An experimental flexion passive motion task is performed, and the kinematic model is tested to determine its capability to reproduce the workspace of the motion task. In addition, an optimization process is performed to simulate prescribed ligament length variations during the motion task. The proposed kinematic model is capable to reproduce with high accuracy an experimental three-dimensional workspace, and at the same time, to simulate prescribed ligament length variation during the spatial flexion task. Prescribed ligament length variations are achieved through an optimization process of the ligament insertion points. This model can be used to improve the multibody kinematic optimization (MKO) process during gait analysis, and also in the design of rehabilitation devices as well as trajectories to accelerate the recovery of injured ligaments. The model shows potential to predict ligament length variations during different motion tasks, and can serve as a basis to develop complex models for kinetostatic and dynamic analyses without dealing with computationally expensive models.

Automatic generation of personalised skeletal models of the lower limb from three-dimensional bone geometries

The generation of personalised and patient-specific musculoskeletal models is currently a cumbersome and time-consuming task that normally requires several processing hours and trained operators. We believe that this aspect discourages the use of computational models even when appropriate data are available and personalised biomechanical analysis would be beneficial. In this paper we present a computational tool that enables the fully automatic generation of skeletal models of the lower limb from three-dimensional bone geometries, normally obtained by segmentation of medical images. This tool was evaluated against four manually created lower limb models finding remarkable agreement in the computed joint parameters, well within human operator repeatability. The coordinate systems origins were identified with maximum differences between 0.5 mm (hip joint) and 5.9 mm (subtalar joint), while the joint axes presented discrepancies between 1° (knee joint) to 11° (subtalar joint). To prove the robustness of the methodology, the models were built from four datasets including both genders, anatomies ranging from juvenile to elderly and bone geometries reconstructed from high-quality computed tomography as well as lower-quality magnetic resonance imaging scans. The entire workflow, implemented in MATLAB scripting language, executed in seconds and required no operator intervention, creating lower extremity models ready to use for kinematic and kinetic analysis or as baselines for more advanced musculoskeletal modelling approaches, of which we provide some practical examples. We auspicate that this technical advancement, together with upcoming progress in medical image segmentation techniques, will promote the use of personalised models in larger-scale studies than those hitherto undertaken.

Accuracy of the tibiofemoral contact forces estimated by a subject-specific musculoskeletal model with fluoroscopy-based contact point trajectories

Accurate estimation of the tibiofemoral contact forces relies on exact kinematics and joint geometry. Subject-specific kinematic constraints representing contact point trajectories derived from fluoroscopic measurements during lunge are introduced in a musculoskeletal model of the lower limb and compared to generic kinematic constraints. The medial, lateral, and total contact forces during gait and squat are validated using the data of four patients with an instrumented prosthesis. The accuracy of the estimated contact forces (both with subject-specific and generic kinematic constraints) remains close to the level reported in the literature. The mean root mean square errors range from 0.32 to 0.52 body weights for gait and from 0.27 to 0.72 body weights for squat. The impact of the subject-specific contact point trajectories is not found substantial or consistent between patients and tasks. Indeed, the kinematics of the total knee prostheses remains close to the kinematics of a hinge joint and the contact point locations remain generally centred at 20 mm from the tibia centreline (close to the constant value defined in the generic constraints). The contact point trajectories are also suspected to differ between tasks (lunge vs. gait and squat). While the contact point trajectories have been reported to be sensitive model parameters, no clear improvement of the contact force accuracy is demonstrated on patients with instrumented prosthesis. The introduction (as kinematic constraints) of fluoroscopy-based contact point trajectories may be considered in cases where these trajectories are significantly altered, as reported for osteoarthritis patients.

Automated creation and tuning of personalised muscle paths for OpenSim musculoskeletal models of the knee joint

Computational modelling is an invaluable tool for investigating features of human locomotion and motor control which cannot be measured except through invasive techniques. Recent research has focussed on creating personalised musculoskeletal models using population-based morphing or directly from medical imaging. Although progress has been made, robust definition of two critical model parameters remains challenging: (1) complete tibiofemoral (TF) and patellofemoral (PF) joint motions, and (2) muscle tendon unit (MTU) pathways and kinematics (i.e. lengths and moment arms). The aim of this study was to develop an automated framework, using population-based morphing approaches to create personalised musculoskeletal models, consisting of personalised bone geometries, TF and PF joint mechanisms, and MTU pathways and kinematics. Informed from medical imaging, personalised rigid body TF and PF joint mechanisms were created. Using atlas- and optimisation-based methods, personalised MTU pathways and kinematics were created with the aim of preventing MTU penetration into bones and achieving smooth MTU kinematics that follow patterns from existing literature. This framework was integrated into the Musculoskeletal Atlas Project Client software package to create and optimise models for 6 participants with incrementally increasing levels of personalisation with the aim of improving MTU kinematics and pathways. Three comparisons were made: (1) non-optimised (Model 1) and optimised models (Model 3) with generic joint mechanisms; (2) non-optimised (Model 2) and optimised models (Model 4) with personalised joint mechanisms; and (3) both optimised models (Model 3 and 4). Following optimisation, improvements were consistently shown in pattern similarity to cadaveric data in comparison (1) and (2). For comparison (3), a number of comparisons showed no significant difference between the two compared models. Importantly, optimisation did not produce statistically significantly worse results in any case.

In Silico-Enhanced Treatment and Rehabilitation Planning for Patients with Musculoskeletal Disorders: Can Musculoskeletal Modelling and Dynamic Simulations Really Impact Current Clinical Practice?

Over the past decades, the use of computational physics-based models representative of the musculoskeletal (MSK) system has become increasingly popular in many fields of clinically driven research, locomotor rehabilitation in particular. These models have been applied to various functional impairments given their ability to estimate parameters which cannot be readily measured in vivo but are of interest to clinicians. The use of MSK modelling and simulations allows analysis of relevant MSK biomarkers such as muscle and joint contact loading at a number of different stages in the clinical treatment pathway in order to benefit patient functional outcome. Applications of these methods include optimisation of rehabilitation programs, patient stratification, disease characterisation, surgical pre-planning, and assistive device and exoskeleton design and optimisation. This review provides an overview of current approaches, the components of standard MSK models, applications, limitations, and assumptions of these modelling and simulation methods, and finally proposes a future direction.

The relationship between tibiofemoral geometry and musculoskeletal function during normal activity

BACKGROUND

The effect of tibiofemoral geometry on musculoskeletal function is important to movement biomechanics.

RESEARCH QUESTION

We hypothesised that tibiofemoral geometry determines tibiofemoral motion and musculoskeletal function. We then aimed at 1) modelling tibiofemoral motion during normal activity as a function of tibiofemoral geometry in healthy adults; and 2) quantifying the effect of tibiofemoral geometry on musculoskeletal function.

METHODS

We used motion data for six activity types and CT images of the knee from 12 healthy adults. Geometrical variation of the tibia and femoral articular surfaces were measured in the CT images. The geometry-based tibiofemoral motion was calculated by fitting a parallel mechanism to geometrical variation in the cohort. Matched musculoskeletal models embedding the geometry-based tibiofemoral joint motion and a common generic tibiofemoral motion of reference were generated and used to calculate joint angles, net joint moments, muscle and joint forces for the six activities analysed. The tibiofemoral model was validated against bi-planar fluoroscopy measurements for walking for all the six planes of motion. The effect of tibiofemoral geometry on musculoskeletal function was the difference between the geometry-based model and the model of reference.

RESULTS

The geometry-based tibiofemoral motion described the pattern and the variation during walking for all six motion components, except the pattern of anterior tibial translation. Tibiofemoral geometry had moderate effect on cohort-averages of musculoskeletal function (R² = 0.60-1), although its effect was high in specific instances of the model, outputs and activities analysed, reaching 2.94 BW for the ankle reaction force during stair descent. In conclusion, tibiofemoral geometry is a major determinant of tibiofemoral motion during walking.

SIGNIFICANCE

Geometrical variations of the tibiofemoral joint are important for studying musculoskeletal function during normal activity in specific individuals but not for studying cohort averages of musculoskeletal function. This finding expands current knowledge of movement biomechanics.

Modelling Human Locomotion to Inform Exercise Prescription for Osteoporosis

PURPOSE OF REVIEW

We review the literature on hip fracture mechanics and models of hip strain during exercise to postulate the exercise regimen for best promoting hip strength.

RECENT FINDINGS

The superior neck is a common location for hip fracture and a relevant exercise target for osteoporosis. Current modelling studies showed that fast walking and stair ambulation, but not necessarily running, optimally load the femoral neck and therefore theoretically would mitigate the natural age-related bone decline, being easily integrated into routine daily activity. High intensity jumps and hopping have been shown to promote anabolic response by inducing high strain in the superior anterior neck. Multidirectional exercises may cause beneficial non-habitual strain patterns across the entire femoral neck. Resistance knee flexion and hip extension exercises can induce high strain in the superior neck when performed using maximal resistance loadings in the average population. Exercise can stimulate an anabolic response of the femoral neck either by causing higher than normal bone strain over the entire hip region or by causing bending of the neck and localized strain in the superior cortex. Digital technologies have enabled studying interdependences between anatomy, bone distribution, exercise, strain and metabolism and may soon enable personalized prescription of exercise for optimal hip strength.