The Capacity of Generic Musculoskeletal Simulations to Predict Knee Joint Loading Using the CAMS-Knee Datasets

Posterior tibial slope influences joint mechanics and soft tissue loading after total knee arthroplasty

As a solution to restore knee function and reduce pain, the demand for Total Knee Arthroplasty (TKA) has dramatically increased in recent decades. The high rates of dissatisfaction and revision makes it crucially important to understand the relationships between surgical factors and post-surgery knee performance. Tibial implant alignment in the sagittal plane (i.e., posterior tibia slope, PTS) is thought to play a key role in quadriceps muscle forces and contact conditions of the joint, but the underlying mechanisms and potential consequences are poorly understood. To address this biomechanical challenge, we developed a subject-specific musculoskeletal model based on the bone anatomy and precise implantation data provided within the CAMS-Knee datasets. Using the novel COMAK algorithm that concurrently optimizes joint kinematics, together with contact mechanics, and muscle and ligament forces, enabled highly accurate estimations of the knee joint biomechanics (RMSE <0.16 BW of joint contact force) throughout level walking and squatting. Once confirmed for accuracy, this baseline modelling framework was then used to systematically explore the influence of PTS on knee joint biomechanics. Our results indicate that PTS can greatly influence tibio-femoral translations (mainly in the anterior-posterior direction), while also suggesting an elevated risk of patellar mal-tracking and instability. Importantly, however, an increased PTS was found to reduce the maximum tibio-femoral contact force and improve efficiency of the quadriceps muscles, while also reducing the patellofemoral contact force (by approximately 1.5% for each additional degree of PTS during walking). This study presents valuable findings regarding the impact of PTS variations on the biomechanics of the TKA joint and thereby provides potential guidance for surgically optimizing implant alignment in the sagittal plane, tailored to the implant design and the individual deficits of each patient.

Residual force depression is not related to positive muscle fascicle work during submaximal voluntary dorsiflexion contractions in humans

Residual force depression (rFD) following active muscle shortening is assumed to correlate most strongly with muscle work, but this has not been tested during voluntary contractions in humans. Using dynamometry, we compared steady-state ankle joint torques (N = 16) following tibialis anterior (TA) muscle-tendon unit (MTU) lengthening and shortening to the time-matched torque during submaximal voluntary fixed-end dorsiflexion reference contractions (REF) at a matched MTU length and EMG amplitude. Ultrasound revealed significantly reduced (P < 0.001) TA fascicle shortening amplitudes during MTU lengthening without a preload over small and medium amplitudes, respectively, relative to REF. MTU lengthening with a preload over a large amplitude significantly (P < 0.001) increased fascicle shortening relative to REF, as well as stretch amplitudes relative to MTU lengthening without a preload (P = 0.001). Significant (P = 0.028) steady-state fascicle force enhancement relative to REF was observed following MTU lengthening, and was similar among MTU lengthening-hold conditions (3-5%). MTU shortening with and without a preload over small and large amplitudes significantly (P < 0.001) increased positive fascicle and MTU work relative to REF, but significant (P = 0.006) rFD was observed following MTU shortening with a preload (7-10%) only. rFD was linearly related to positive MTU work [rrm (47) = 0.48, P < 0.001], but not positive fascicle work [rrm (47) = 0.16, P = 0.277]. Our findings indicate that MTU lengthening without substantial fascicle stretch enhances steady-state force output, which might arise from less shortening-induced rFD. Our findings also indicate similar rFD following different amounts of positive fascicle/MTU work, which cautions against using work to predict rFD during submaximal voluntary contractions. KEY POINTS: Accurately predicting muscle force is challenging because active muscle shortening depresses force output. The residual force depression (rFD) that exists following active muscle shortening is commonly assumed to correlate strongly and positively with muscle work. We found that tibialis anterior muscle fascicle work and muscle-tendon unit work did not accurately predict rFD during submaximal voluntary dorsiflexion contractions. Fascicle shortening during fixed-end reference contractions also potentially induced rFD of 3-5%, which was similar to the rFD following muscle-tendon unit shortening without a preload. A higher number of active muscle fibres during shortening probably increased rFD, which suggests that motor unit recruitment during shortening might predict rFD.

Comparing three generic musculoskeletal models to estimate the tibiofemoral reaction forces during gait and sit-to-stand tasks

The choice of musculoskeletal (MSK) model is crucial for performing MSK estimations to evaluate muscle demands and joint forces. This study compared two previously published generic MSK models and a modified model to estimate tibiofemoral reaction forces (TFRF) during gait, sit-to-stand, and stand-to-sit. The estimated tibiofemoral reaction forces were compared with an in vivo dataset from six patients using an instrumented knee prosthesis. A correlation and root mean square error (RMSE) in the time-series analysis and relative peak error (RPE) were evaluated. The results showed that the three MSK models were similar in estimating the vertical forces, with a large correlation, and RPE was found around 20 % during gait. The RMSE and the RPE indicated that the modified model had lower total and lateral compartment forces errors for sit-to-stand and stand-to-sit, showing the best performance. The shear forces for all tasks and models showed significant errors. Future MSK studies should consider these findings when researching functional tasks. The modified model was found to be more effective in estimating the vertical tibiofemoral joint reaction forces in tasks that impose greater demands on muscle forces and require high knee and hip flexion.

Medial and lateral knee contact forces during walking, stair ascent and stair descent are more affected by contact locations than tibiofemoral alignment in knee osteoarthritis patients with varus malalignment

Introduction: Knee OA progression is related to medial knee contact forces, which can be altered by anatomical parameters of tibiofemoral alignment and contact point locations. There is limited and controversial literature on medial-lateral force distribution and the effect of anatomical parameters, especially in motor activities different from walking. We analyzed the effect of tibiofemoral alignment and contact point locations on knee contact forces, and the medial-lateral force distribution in knee OA subjects with varus malalignment during walking, stair ascending and stair descending. Methods: Fifty-one knee OA subjects with varus malalignment underwent weight-bearing radiographs and motion capture during walking, stair ascending and stair descending. We created a set of four musculoskeletal models per subject with increasing level of personalization, and calculated medial and lateral knee contact forces. To analyze the effect of the anatomical parameters, statistically-significant differences in knee contact forces among models were evaluated. Then, to analyze the force distribution, the medial-to-total contact force ratios were calculated from the fully-informed models. In addition, a multiple regression analysis was performed to evaluate correlations between forces and anatomical parameters. Results: The anatomical parameters significantly affected the knee contact forces. However, the contact points decreased medial forces and increased lateral forces and led to more marked variations compared to tibiofemoral alignment, which produced an opposite effect. The forces were less medially-distributed during stair negotiation, with medial-to-total ratios below 50% at force peaks. The anatomical parameters explained 30%-67% of the variability in the knee forces, where the medial contact points were the best predictors of medial contact forces. Discussion: Including personalized locations of contact points is crucial when analyzing knee contact forces in subjects with varus malalignment, and especially the medial contact points have a major effect on the forces rather than tibiofemoral alignment. Remarkably, the medial-lateral force distribution depends on the motor activity, where stair ascending and descending show increased lateral forces that lead to less medially-distributed loads compared to walking.

The influence of induced gait asymmetry on joint reaction forces

Chronic injury- or disease-induced joint impairments result in asymmetric gait deviations that may precipitate changes in joint loading associated with pain and osteoarthritis. Understanding the impact of gait deviations on joint reaction forces (JRFs) is challenging because of concurrent neurological and/or anatomical changes and because measuring JRFs requires medically invasive instrumented implants. Instead, we investigated the impact of joint motion limitations and induced asymmetry on JRFs by simulating data recorded as 8 unimpaired participants walked with bracing to unilaterally and bilaterally restrict ankle, knee, and simultaneous ankle + knee motion. Personalized models, calculated kinematics, and ground reaction forces (GRFs) were input into a computed muscle control tool to determine lower limb JRFs and simulated muscle activations guided by electromyography-driven timing constraints. Unilateral knee restriction increased GRF peak and loading rate ipsilaterally but peak values decreased contralaterally when compared to walking without joint restriction. GRF peak and loading rate increased with bilateral restriction compared to the contralateral limb of unilaterally restricted conditions. Despite changes in GRFs, JRFs were relatively unchanged due to reduced muscle forces during loading response. Thus, while joint restriction results in increased limb loading, reductions in muscle forces counteract changes in limb loading such that JRFs were relatively unchanged.

A novel method to accurately recreate in vivo loads and kinematics in computational models of the knee

Despite availability of in vivo knee loads and kinematics data, conventional load- and displacement-controlled configurations still can't accurately predict tibiofemoral loads from kinematics or vice versa. We propose a combined load- and displacement-control method for joint-level simulations of the knee to reliably reproduce in vivo contact mechanics. Prediction errors of the new approach were compared to those of conventional purely load- or displacement-controlled models using in vivo implant loads and kinematics for multiple subjects and activities (CAMS-Knee dataset). Our method reproduced both loads and kinematics more closely than conventional models and thus demonstrates clear advantages for investigating tibiofemoral contact or wear.

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.

On the estimation of hip joint loads through musculoskeletal modeling

Noninvasive estimation of joint loads is still an open challenge in biomechanics. Although musculoskeletal modeling represents a solid resource, multiple improvements are still necessary to obtain accurate predictions of joint loads and to translate such potential into practical utility. The present study, focused on the hip joint, is aimed at reviewing the state-of-the-art literature on the estimation of hip joint reaction forces through musculoskeletal modeling. Our literature inspection, based on well-defined selection criteria, returned seventeen works, which were compared in terms of methods and results. Deviations between predicted and in vivo measured hip joint loads, taken from the OrthoLoad database, were assessed through quantitative deviation indices. Despite the numerous modeling and computational improvements made over the last two decades, predicted hip joint loads still deviate from their experimental counterparts and typically overestimate them. Several critical aspects have emerged that affect muscle force estimation, hence joint loads. Among them, the physical fidelity of the musculoskeletal model, with its parameters and geometry, plays a crucial role. Also, predicted joint loads are markedly affected by the selected muscle recruitment strategy, which reflects the underlying motor control policy. Practical guidelines for researchers interested in noninvasive estimation of hip joint loads are also provided.

Multi-level personalization of neuromusculoskeletal models to estimate physiologically plausible knee joint contact forces in children

Neuromusculoskeletal models are a powerful tool to investigate the internal biomechanics of an individual. However, commonly used neuromusculoskeletal models are generated via linear scaling of generic templates derived from elderly adult anatomies and poorly represent a child, let alone children with a neuromuscular disorder whose musculoskeletal structures and muscle activation patterns are profoundly altered. Model personalization can capture abnormalities and appropriately describe the underlying (altered) biomechanics of an individual. In this work, we explored the effect of six different levels of neuromusculoskeletal model personalization on estimates of muscle forces and knee joint contact forces to tease out the importance of model personalization for normal and abnormal musculoskeletal structures and muscle activation patterns. For six children, with and without cerebral palsy, generic scaled models were developed and progressively personalized by (1) tuning and calibrating musculotendon units' parameters, (2) implementing an electromyogram-assisted approach to synthesize muscle activations, and (3) replacing generic anatomies with image-based bony geometries, and physiologically and physically plausible muscle kinematics. Biomechanical simulations of gait were performed in the OpenSim and CEINMS software on ten overground walking trials per participant. A mixed-ANOVA test, with Bonferroni corrections, was conducted to compare all models' estimates. The model with the highest level of personalization produced the most physiologically plausible estimates. Model personalization is crucial to produce physiologically plausible estimates of internal biomechanical quantities. In particular, personalization of musculoskeletal anatomy and muscle activation patterns had the largest effect overall. Increased research efforts are needed to ease the creation of personalized neuromusculoskeletal models.

A Decoding Prediction Model of Flexion and Extension of Left and Right Feet from Electroencephalogram

Detection of limb motor functions utilizing brain signals is a significant technique in the brain signal gain model (BSM) that can be effectively employed in various biomedical applications. Our research presents a novel technique for prediction of feet motor functions by applying a deep learning model with cascading transfer learning technique to use the electroencephalogram (EEG) in the training stage. Our research deduces the electroencephalogram data (EEG) of stroke incidence to propose functioning high-tech interfaces for predicting left and right foot motor functions. This paper presents a transfer learning with several source input domains to serve a target domain with small input size. Transfer learning can reduce the learning curve effectively. The correctness of the presented model is evaluated by the abilities of motor functions in the detection of left and right feet. Extensive experiments were performed and proved that a higher accuracy was reached by the introduced BSM-EEG neural network with transfer learning. The prediction of the model accomplished 97.5% with less CPU time. These accurate results confirm that the BSM-EEG neural model has the ability to predict motor functions for brain-injured stroke therapy.

Uncertainty in Muscle–Tendon Parameters can Greatly Influence the Accuracy of Knee Contact Force Estimates of Musculoskeletal Models

Understanding the sources of error is critical before models of the musculoskeletal system can be usefully translated. Using in vivo measured tibiofemoral forces, the impact of uncertainty in muscle–tendon parameters on the accuracy of knee contact force estimates of a generic musculoskeletal model was investigated following a probabilistic approach. Population variability was introduced to the routine musculoskeletal modeling framework by perturbing input parameters of the lower limb muscles around their baseline values. Using ground reaction force and skin marker trajectory data collected from six subjects performing body-weight squat, the knee contact force was calculated for the perturbed models. The combined impact of input uncertainties resulted in a considerable variation in the knee contact force estimates (up to 2.1 BW change in the predicted force), especially at larger knee flexion angles, hence explaining up to 70% of the simulation error. Although individual muscle groups exhibited different contributions to the overall error, variation in the maximum isometric force and pathway of the muscles showed the highest impacts on the model outcomes. Importantly, this study highlights parameters that should be personalized in order to achieve the best possible predictions when using generic musculoskeletal models for activities involving deep knee flexion.

An EMG-assisted muscle-force driven finite element analysis pipeline to investigate joint- and tissue-level mechanical responses in functional activities: towards a rapid assessment toolbox

Joint tissue mechanics (e.g., stress and strain) are believed to have a major involvement in the onset and progression of musculoskeletal disorders, e.g., knee osteoarthritis (KOA). Accordingly, considerable efforts have been made to develop musculoskeletal finite element (MS-FE) models to estimate highly detailed tissue mechanics that predict cartilage degeneration. However, creating such models is time-consuming and requires advanced expertise. This limits these complex, yet promising MS-FE models to research applications with few participants and makes the models impractical for clinical assessments. Also, these previously developed MS-FE models have not been used to assess activities other than gait. This study introduces and verifies a semi-automated rapid state-of-the-art MS-FE modeling and simulation toolbox incorporating an electromyography- (EMG) assisted MS model and a muscle-force driven FE model of the knee with fibril-reinforced poro(visco)elastic cartilages and menisci. To showcase the usability of the pipeline, we estimated joint- and tissue-level knee mechanics in 15 KOA individuals performing different daily activities. The pipeline was verified by comparing the estimated muscle activations and joint mechanics to existing experimental data. To determine the importance of EMG-assisted MS approach, results were compared to those from the same FE models but driven by static-optimization-based MS models. The EMG-assisted MS-FE pipeline bore a closer resemblance to experiments compared to the static-optimization-based MS-FE pipeline. Importantly, the developed pipeline showed great potential as a rapid MS-FE analysis toolbox to investigate multiscale knee mechanics during different activities of individuals with KOA. The template FE model of the study is freely available here.

Optimization Reduces Knee-Joint Forces During Walking and Squatting: Validating the Inverse Dynamics Approach for Full Body Movements on Instrumented Knee Prostheses

Because of the redundancy of our motor system, movements can be performed in many ways. While multiple motor control strategies can all lead to the desired behavior, they result in different joint and muscle forces. This creates opportunities to explore this redundancy, for example, for pain avoidance or reducing the risk of further injury. To assess the effect of different motor control optimization strategies, a direct measurement of muscle and joint forces is desirable, but problematic for medical and ethical reasons. Computational modeling might provide a solution by calculating approximations of these forces. In this study, we used a full-body computational musculoskeletal model to (a) predict forces measured in knee prostheses during walking and squatting and (b) study the effect of different motor control strategies (i.e., minimizing joint force vs. muscle activation) on the joint load and prediction error. We found that musculoskeletal models can accurately predict knee joint forces with a root mean squared error of <0.5 body weight (BW) in the superior direction and about 0.1 BW in the medial and anterior directions. Generally, minimization of joint forces produced the best predictions. Furthermore, minimizing muscle activation resulted in maximum knee forces of about 4 BW for walking and 2.5 BW for squatting. Minimizing joint forces resulted in maximum knee forces of 2.25 BW and 2.12 BW, that is, a reduction of 44% and 15%, respectively. Thus, changing the muscular coordination strategy can strongly affect knee joint forces. Patients with a knee prosthesis may adapt their neuromuscular activation to reduce joint forces during locomotion.

Alterations in the Functional Knee Alignment Are Not an Effective Strategy to Modify the Mediolateral Distribution of Knee Forces During Closed Kinetic Chain Exercises

Pain felt while performing rehabilitation exercises could be a reason for the low adherence of knee osteoarthritis patients to physical rehabilitation. Reducing compressive forces on the most affected knee regions may help to mitigate the pain. Knee frontal plane positioning with respect to pelvis and foot (functional knee alignment) has been shown to modify the mediolateral distribution of the tibiofemoral joint contact force in walking. Hence, different functional knee alignments could be potentially used to modify joint loading during rehabilitation exercises. The aim was to understand whether utilizing different alignments is an effective strategy to unload specific knee areas while performing rehabilitation exercises. Eight healthy volunteers performed 5 exercises with neutral, medial, and lateral knee alignment. A musculoskeletal model was modified for improved prediction of tibiofemoral contact forces and used to evaluate knee joint kinematics, moments, and contact forces. Functional knee alignment had only a small and inconsistent effect on the mediolateral distribution joint contact force. Moreover, the magnitude of tibiofemoral and patellofemoral contact forces, knee moments, and measured muscle activities was not significantly affected by the alignment. Our results suggest that altering the functional knee alignment is not an effective strategy to unload specific knee regions in physical rehabilitation.

Acute Vibration Feedback During Gait Reduces Mechanical Ankle Joint Loading in Chronic Ankle Instability Patients

BACKGROUND

Individuals with chronic ankle instability (CAI) exhibit altered vertical ground reaction forces (vGRF), a laterally shifted center of pressure, and an inverted foot position during walking. These neuromechanical alterations are linked with altered ankle joint loading in this population. Vibration-based gait retraining improves center of pressure positioning but effects on neuromechanical variables influencing joint loading remains unknown.

RESEARCH QUESTION

Do patients with CAI exhibit altered vGRF and ankle joint contact forces (JCF) after receiving a single session of vibration-based gait retraining?

METHODS

Ten individuals with CAI underwent a single session of vibration-based gait retraining. Kinematic and kinetic data were collected during walking on an instrumental treadmill with force plates embedded in it. Following a baseline gait assessment without feedback, participants walked at a self-selected speed for 10 minutes while receiving feedback. Data was collected during an early (1 st and 2 nd minute) and late adaptation phase (9 th and 10 th minute) and, compared to baseline values. Impact and propulsive vGRF variables (i.e. peak, time to peak, and loading rate) were obtained. Musculoskeletal modeling was used to calculate ankle JCF variables (peak, impulse, and loading rate) during stance phase.

RESULTS

Propulsive vGRF and ankle JCF outcomes were significantly reduced during the early and late adaptation phases (p ≤ 0.039).

SIGNIFICANCE

These results indicate that vibration-based gait retraining can immediately reduce propulsive vGRF and ankle JCF and may represent a modality that could help restore appropriate ankle joint loading patterns in those with CAI.

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.

12 Degrees of Freedom Muscle Force Driven Fibril-Reinforced Poroviscoelastic Finite Element Model of the Knee Joint

Accurate knowledge of the joint kinematics, kinetics, and soft tissue mechanical responses is essential in the evaluation of musculoskeletal (MS) disorders. Since in vivo measurement of these quantities requires invasive methods, musculoskeletal finite element (MSFE) models are widely used for simulations. There are, however, limitations in the current approaches. Sequentially linked MSFE models benefit from complex MS and FE models; however, MS model's outputs are independent of the FE model calculations. On the other hand, due to the computational burden, embedded (concurrent) MSFE models are limited to simple material models and cannot estimate detailed responses of the soft tissue. Thus, first we developed a MSFE model of the knee with a subject-specific MS model utilizing an embedded 12 degrees of freedom (DoFs) knee joint with elastic cartilages in which included both secondary kinematic and soft tissue deformations in the muscle force estimation (inverse dynamics). Then, a muscle-force-driven FE model with fibril-reinforced poroviscoelastic cartilages and fibril-reinforced poroelastic menisci was used in series to calculate detailed tissue mechanical responses (forward dynamics). Second, to demonstrate that our workflow improves the simulation results, outputs were compared to results from the same FE models which were driven by conventional MS models with a 1 DoF knee, with and without electromyography (EMG) assistance. The FE model driven by both the embedded and the EMG-assisted MS models estimated similar results and consistent with experiments from literature, compared to the results estimated by the FE model driven by the MS model with 1 DoF knee without EMG assistance.

Estimating Knee Joint Load Using Acoustic Emissions During Ambulation

Quantifying joint load in activities of daily life could lead to improvements in mobility for numerous people; however, current methods for assessing joint load are unsuitable for ubiquitous settings. The aim of this study is to demonstrate that joint acoustic emissions contain information to estimate this internal joint load in a potentially wearable implementation. Eleven healthy, able-bodied individuals performed ambulation tasks under varying speed, incline, and loading conditions while joint acoustic emissions and essential gait measures-electromyography, ground reaction forces, and motion capture trajectories-were collected. The gait measures were synthesized using a neuromuscular model to estimate internal joint contact force which was the target variable for subject-specific machine learning models (XGBoost) trained based on spectral, temporal, cepstral, and amplitude-based features of the joint acoustic emissions. The model using joint acoustic emissions significantly outperformed (p < 0.05) the best estimate without the sounds, the subject-specific average load (MAE = 0.31 ± 0.12 BW), for both seen (MAE = 0.08 ± 0.01 BW) and unseen (MAE = 0.21 ± 0.05 BW) conditions. This demonstrates that joint acoustic emissions contain information that correlates to internal joint contact force and that information is consistent such that unique cases can be estimated.

Subject-specific muscle properties from diffusion tensor imaging significantly improve the accuracy of musculoskeletal models

Musculoskeletal modelling is an important platform on which to study the biomechanics of morphological structures in vertebrates and is widely used in clinical, zoological and palaeontological fields. The popularity of this approach stems from the potential to non-invasively quantify biologically important but difficult-to-measure functional parameters. However, while it is known that model predictions are highly sensitive to input values, it is standard practice to build models by combining musculoskeletal data from different sources resulting in 'generic' models for a given species. At present, there are little quantitative data on how merging disparate anatomical data in models impacts the accuracy of these functional predictions. This issue is addressed herein by quantifying the accuracy of both subject-specific human limb models containing individualised muscle force-generating properties and models built using generic properties from both elderly and young individuals, relative to experimental muscle torques obtained from an isokinetic dynamometer. The results show that subject-specific models predict isokinetic muscle torques to a greater degree of accuracy than generic models at the ankle (root-mean-squared error - 7.9% vs. 49.3% in elderly anatomy-based models), knee (13.2% vs. 57.3%) and hip (21.9% vs. 32.8%). These results have important implications for the choice of musculoskeletal properties in future modelling studies, and the relatively high level of accuracy achieved in the subject-specific models suggests that such models can potentially address questions about inter-subject variations of muscle functions. However, despite relatively high levels of overall accuracy, models built using averaged generic muscle architecture data from young, healthy individuals may lack the resolution and accuracy required to study such differences between individuals, at least in certain circumstances. The results do not wholly discourage the continued use of averaged generic data in musculoskeletal modelling studies but do emphasise the need for to maximise the accuracy of input values if studying intra-species form-function relationships in the musculoskeletal system.