Influence of musculoskeletal model parameter values on prediction of accurate knee contact forces during walking

In-silico study of the biomechanical effects of proximal-fibular osteotomy on knee joint contact pressure in varus-valgus misalignment

The aim of this work is to investigate in-silico the biomechanical effects of a proximal fibular osteotomy (PFO) on a knee joint with different varus/valgus deformities on the progression of knee osteoarthritis (KOA). A finite element analysis (FEA) of a human lower extremity consisting of the femoral, tibial and fibular bones and the cartilage connecting them was designed. The FEA was performed in a static standing primitive position to determine the contact pressure (CP) distribution and the location of the center of pressure (CoP). The analysis examined the relationship between these factors and the degree of deformation of the hip-knee angle in the baseline condition. The results suggested that PFO could be a simple and effective surgical treatment for patients with associated genu varum. This work also reported that a possible CP homogenization and a CoP correction can be achieved for medial varus deformities after PFO. However, it reduced its effectiveness for tibial origin valgus misalignment and worsened in cases of femoral valgus misalignment.

How effective is proximal fibular osteotomy in redistributing joint pressures? Insights from an HTO comparative in-silico study

BACKGROUND

Knee osteoarthritis (KOA) represents a widespread degenerative condition among adults that significantly affects quality of life. This study aims to elucidate the biomechanical implications of proximal fibular osteotomy (PFO), a proposed cost-effective and straightforward intervention for KOA, comparing its effects against traditional high tibial osteotomy (HTO) through in-silico analysis.

METHODS

Using medical imaging and finite element analysis (FEA), this research quantitatively evaluates the biomechanical outcomes of a simulated PFO procedure in patients with severe medial compartment genu-varum, who have undergone surgical correction with HTO. The study focused on evaluating changes in knee joint contact pressures, stress distribution, and anatomical positioning of the center of pressure (CoP). Three models are generated for each of the five patients investigated in this study, a preoperative original condition model, an in-silico PFO based on the same original condition data, and a reversed-engineered HTO in-silico model.

RESULTS

The novel contribution of this investigation is the quantitative analysis of the impact of PFO on the biomechanics of the knee joint. The results provide mechanical evidence that PFO can effectively redistribute and homogenize joint stresses, while also repositioning the CoP towards the center of the knee, similar to what is observed post HTO. The findings propose PFO as a potentially viable and simpler alternative to conventional surgical methods for managing severe KOA, specifically in patients with medial compartment genu-varum.

CONCLUSION

This research also marks the first application of FEA that may support one of the underlying biomechanical theories of PFO, providing a foundation for future clinical and in-silico studies.

Optimal fibre length and maximum isometric force are the most influential parameters when modelling muscular adaptations to unloading using Hill-type muscle models

Introduction: Spaceflight is associated with severe muscular adaptations with substantial inter-individual variability. A Hill-type muscle model is a common method to replicate muscle physiology in musculoskeletal simulations, but little is known about how the underlying parameters should be adjusted to model adaptations to unloading. The aim of this study was to determine how Hill-type muscle model parameters should be adjusted to model disuse muscular adaptations. Methods: Isokinetic dynamometer data were taken from a bed rest campaign and used to perform tracking simulations at two knee extension angular velocities (30°·s-1 and 180°·s-1). The activation and contraction dynamics were solved using an optimal control approach and direct collocation method. A Monte Carlo sampling technique was used to perturb muscle model parameters within physiological boundaries to create a range of theoretical and feasible parameters to model muscle adaptations. Results: Optimal fibre length could not be shortened by more than 67% and 61% for the knee flexors and non-knee muscles, respectively. Discussion: The Hill-type muscle model successfully replicated muscular adaptations due to unloading, and recreated salient features of muscle behaviour associated with spaceflight, such as altered force-length behaviour. Future researchers should carefully adjust the optimal fibre lengths of their muscle-models when trying to model adaptations to unloading, particularly muscles that primarily operate on the ascending and descending limbs of the force-length relationship.

A novel computational framework for the estimation of internal musculoskeletal loading and muscle adaptation in hypogravity

Introduction: Spaceflight is associated with substantial and variable musculoskeletal (MSK) adaptations. Characterisation of muscle and joint loading profiles can provide key information to better align exercise prescription to astronaut MSK adaptations upon return-to-Earth. A case-study is presented of single-leg hopping in hypogravity to demonstrate the additional benefit computational MSK modelling has when estimating lower-limb MSK loading. Methods: A single male participant performed single-leg vertical hopping whilst attached to a body weight support system to replicate five gravity conditions (0.17, 0.25, 0.37, 0.50, 1 g). Experimental joint kinematics, joint kinetics and ground reaction forces were tracked in a data-tracking direct collocation simulation framework. Ground reaction forces, sagittal plane hip, knee and ankle net joint moments, quadriceps muscle forces (Rectus Femoris and three Vasti muscles), and hip, knee and ankle joint reaction forces were extracted for analysis. Estimated quadriceps muscle forces were input into a muscle adaptation model to predict a meaningful increase in muscle cross-sectional area, defined in (DeFreitas et al., 2011). Results: Two distinct strategies were observed to cope with the increase in ground reaction forces as gravity increased. Hypogravity was associated with an ankle dominant strategy with increased range of motion and net plantarflexor moment that was not seen at the hip or knee, and the Rectus Femoris being the primary contributor to quadriceps muscle force. At 1 g, all three joints had increased range of motion and net extensor moments relative to 0.50 g, with the Vasti muscles becoming the main muscles contributing to quadriceps muscle force. Additionally, hip joint reaction force did not increase substantially as gravity increased, whereas the other two joints increased monotonically with gravity. The predicted volume of exercise needed to counteract muscle adaptations decreased substantially with gravity. Despite the ankle dominant strategy in hypogravity, the loading on the knee muscles and joint also increased, demonstrating this provided more information about MSK loading. Discussion: This approach, supplemented with muscle-adaptation models, can be used to compare MSK loading between exercises to enhance astronaut exercise prescription.

A novel computational workflow to holistically assess total knee arthroplasty biomechanics identifies subject-specific effects of joint mechanics on implant fixation

Computational studies of total knee arthroplasty (TKA) often focus on either joint mechanics (kinematics and forces) or implant fixation mechanics. However, such disconnect between joint and fixation mechanics hinders our understanding of overall TKA biomechanical function by preventing identification of key relationships between these two levels of TKA mechanics. We developed a computational workflow to holistically assess TKA biomechanics by integrating musculoskeletal and finite element (FE) models. For our initial study using the workflow, we investigated how tibiofemoral contact mechanics affected the risk of failure due to debonding at the implant-cement interface using the four available subjects from the Grand Challenge Competitions to Predict In Vivo Knee Loads. We used a musculoskeletal model with a 12 degrees-of-freedom knee joint to simulate the stance phase of gait for each subject. The computed tibiofemoral joint forces at each node in contact were direct inputs to FE simulations of the same subjects. We found that the peak risk of failure did not coincide with the peak joint forces or the extreme tibiofemoral contact positions. Moreover, despite the consistency of joint forces across subjects, we observed important variability in the profile of the risk of failure during gait. Thus, by a combined evaluation of the joint and implant fixation mechanics of TKA, we could identify subject-specific effects of joint kinematics and forces on implant fixation that would otherwise have gone unnoticed. We intend to apply our workflow to evaluate the impact of implant alignment and design on TKA biomechanics.

Video-driven simulation of lower limb mechanical loading during aquatic exercises

Understanding the mechanical demands of an exercise on the musculoskeletal system is crucial to prescribe effective training or therapeutic interventions. Yet, that knowledge is currently limited in water, mostly because of the difficulty in evaluating external resistance. Here I reconcile recent advances in 3D markerless pose and mesh estimation, biomechanical simulations, and hydrodynamic modeling, to predict lower limb mechanical loading during aquatic exercises. Simulations are driven exclusively from a single video. Fluid forces were estimated within 12.5±4.1% of the peak forces determined through computational fluid dynamics analyses, at a speed three orders of magnitude greater. In silico hip and knee resultant joint forces agreed reasonably well with in vivo instrumented implant recordings (R²=0.74) downloaded from the OrthoLoad database, both in magnitude (RMSE =251±125 N) and direction (cosine similarity = 0.92±0.09). Hip flexors, glutes, adductors, and hamstrings were the main contributors to hip joint compressive forces (40.4±12.7%, 25.6±9.7%, 14.2±4.8%, 13.0±8.2%, respectively), while knee compressive forces were mostly produced by the gastrocnemius (39.1±15.9%) and vasti (29.4±13.7%). Unlike dry-land locomotion, non-hip- and non-knee-spanning muscles provided little to no offloading effect via dynamic coupling. This noninvasive method has the potential to standardize the reporting of exercise intensity, inform the design of rehabilitation protocols and improve their reproducibility.

Neuromusculoskeletal model calibration accounts for differences in electromechanical delay and maximum isometric muscle force

Electromechanical delay (EMD) and maximum isometric muscle force (F_o^M) are important parameters for joint contact force calculation with EMG-informed neuromusculoskeletal (NMS) models. These parameters can vary between tasks (EMD) and individuals (EMD and F_o^M), making it challenging to establish representative values. One promising approach is to personalise candidate parameters to the participant (e.g., F_o^M by regression equation) and then adjust all parameters within a calibration (i.e., numerical optimisation) to minimise error between corresponding pairs of experimental measures and model-predicted values. The purpose of this study was to determine whether calibration of an NMS model resulted in consistent joint contact forces, regardless of EMD value or personalisation of F_o^M. Hip, knee, and ankle contact forces were predicted for 28 participants using EMG-informed NMS models. Differences in joint contact forces with EMD were examined in six models, calibrated with EMD from 15 to 110 ms. Differences in joint contact forces with personalisation of F_o^M were examined in two models, both calibrated with the same initial EMD (50 ms), one with generic and one with personalised values for F_o^M. For all models, joint contact force peaks during the first and second halves of stance were extracted and compared using a repeated-measures analysis of variance. Calibrated models with EMD set between 35 and 70 ms produced similar magnitude and timing of peak joint contact forces. Compared with generic values, personalising and then calibrating F_o^M resulted in comparable peak contact forces at hip, but not knee or ankle, while also producing muscle-specific tensions similar to reported literature. Overall, EMD between 35 and 70 ms and personalised initial values of F_o^M before calibration are advised for EMG-informed NMS modelling.

InverseMuscleNET: Alternative Machine Learning Solution to Static Optimization and Inverse Muscle Modeling

InverseMuscleNET, a machine learning model, is proposed as an alternative to static optimization for resolving the redundancy issue in inverse muscle models. A recurrent neural network (RNN) was optimally configured, trained, and tested to estimate the pattern of muscle activation signals. Five biomechanical variables (joint angle, joint velocity, joint acceleration, joint torque, and activation torque) were used as inputs to the RNN. A set of surface electromyography (EMG) signals, experimentally measured around the shoulder joint for flexion/extension, were used to train and validate the RNN model. The obtained machine learning model yields a normalized regression in the range of 88-91% between experimental data and estimated muscle activation. A sequential backward selection algorithm was used as a sensitivity analysis to discover the less dominant inputs. The order of most essential signals to least dominant ones was as follows: joint angle, activation torque, joint torque, joint velocity, and joint acceleration. The RNN model required 0.06 s of the previous biomechanical input signals and 0.01 s of the predicted feedback EMG signals, demonstrating the dynamic temporal relationships of the muscle activation profiles. The proposed approach permits a fast and direct estimation ability instead of iterative solutions for the inverse muscle model. It raises the possibility of integrating such a model in a real-time device for functional rehabilitation and sports evaluation devices with real-time estimation and tracking. This method provides clinicians with a means of estimating EMG activity without an invasive electrode setup.

Consistent inconsistencies in braking: a spatial analysis

The dynamic system that is the bipedal body in motion is of interest to engineers, clinicians and biological anthropologists alike. Spatial statistics is more familiar to public health researchers as a way of analysing disease clustering and spread; nonetheless, this is a practical approach to the two-dimensional topography of the foot. We quantified the clustering of the centre of pressure (CoP) on the foot for peak braking and propulsive vertical ground reaction forces (GRFs) over multiple, contiguous steps to assess the consistency of the location of peak forces on the foot during walking. The vertical GRFs of 11 participants were collected continuously via a wireless insole system (MoticonReGo AG) across various experimental conditions. We hypothesized that CoPs would cluster in the hindfoot for braking and forefoot for propulsion, and that braking would demonstrate more consistent clustering than propulsion. Contrary to our hypotheses, we found that CoPs during braking are inconsistent in their location, and CoPs during propulsion are more consistent and clustered across all participants and all trials. These results add to our understanding of the applied forces on the foot so that we can better predict fatigue failures and better understand the mechanisms that shaped the modern bipedal form.

A factorization-based algorithm to predict EMG data using only kinematics information

EMG analyses have several applications, such as identifying muscle excitation patterns during rehabilitation or training plans, or controlling EMG-driven devices. However, experimental measurements can be time consuming or difficult to obtain. This study presents a simple algorithm to predict EMG signals that can be applied in real time during running, given only the instantaneous vector of kinematics. We hypothesize that the factorization of the kinematics of the skeleton together with the EMG data of calibration subjects could be used to predict EMG data of another subject only using the kinematic information. The results showed that EMG signals of lower-limb muscles can be predicted accurately in less than a second using this method. Correlation coefficients between predicted and experimental EMG signals were higher than 0.7 in 10 out of 11 muscles for most prediction trials and subjects, and their overall median value was higher than 0.8. These values confirm that this method could be used to accurately predict EMG signals in real time when only kinematics are measured.

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.