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

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

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

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

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

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

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

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

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

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

A novel computational framework for deducing muscle synergies from experimental joint moments

Prior experimental studies have hypothesized the existence of a “muscle synergy” based control scheme for producing limb movements and locomotion in vertebrates. Such synergies have been suggested to consist of fixed muscle grouping schemes with the co-activation of all muscles in a synergy resulting in limb movement. Quantitative representations of these groupings (termed muscle weightings) and their control signals (termed synergy controls) have traditionally been derived by the factorization of experimentally measured EMG. This study presents a novel approach for deducing these weightings and controls from inverse dynamic joint moments that are computed from an alternative set of experimental measurements—movement kinematics and kinetics. This technique was applied to joint moments for healthy human walking at 0.7 and 1.7 m/s, and two sets of “simulated” synergies were computed based on two different criteria (1) synergies were required to minimize errors between experimental and simulated joint moments in a musculoskeletal model (pure-synergy solution) (2) along with minimizing joint moment errors, synergies also minimized muscle activation levels (optimal-synergy solution). On comparing the two solutions, it was observed that the introduction of optimality requirements (optimal-synergy) to a control strategy solely aimed at reproducing the joint moments (pure-synergy) did not necessitate major changes in the muscle grouping within synergies or the temporal profiles of synergy control signals. Synergies from both the simulated solutions exhibited many similarities to EMG derived synergies from a previously published study, thus implying that the analysis of the two different types of experimental data reveals similar, underlying synergy structures

Generative adversarial networks to create synthetic motion capture datasets including subject and gait characteristics

\ua9 2024 The AuthorsResource-intensive motion capture (mocap) systems challenge predictive deep learning applications, requiring large and diverse datasets. We tackled this by modifying generative adversarial networks (GANs) into conditional GANs (cGANs) that can generate diverse mocap data, including 15 marker trajectories, lower limb joint angles, and 3D ground reaction forces (GRFs), based on specified subject and gait characteristics. The cGAN comprised 1) an encoder compressing mocap data to a latent vector, 2) a decoder reconstructing the mocap data from the latent vector with specific conditions and 3) a discriminator distinguishing random vectors with conditions from encoded latent vectors with conditions. Single-conditional models were trained separately for age, sex, leg length, mass, and walking speed, while an additional model (Multi-cGAN) combined all conditions simultaneously to generate synthetic data. All models closely replicated the training dataset (&lt;8.1 % of the gait cycle different between experimental and synthetic kinematics and GRFs), while a subset with narrow condition ranges was best replicated by the Multi-cGAN, producing similar kinematics (&lt;1\ub0) and GRFs (&lt;0.02 body-weight) averaged by walking speeds. Multi-cGAN also generated synthetic datasets and results for three previous studies using reported mean and standard deviation of subject and gait characteristics. Additionally, unseen test data was best predicted by the walking speed-conditional, showcasing synthetic data diversity. The same model also matched the dynamical consistency of the experimental data (32 % average difference throughout the gait cycle), meaning that transforming the gait cycle data to the original time domain yielded accurate derivative calculations. Importantly, synthetic data poses no privacy concerns, potentially facilitating data sharing

Using musculoskeletal models to estimate in vivo total knee replacement kinematics and loads: effect of differences between models

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

Muscle recruitment strategies can reduce joint loading during level walking

Joint inflammation, with consequent cartilage damage and pain, typically reduces functionality and affects activities of daily life in a variety of musculoskeletal diseases. Since mechanical loading is an important determinant of the disease process, a possible conservative treatment is the unloading of joints. In principle, a neuromuscular rehabilitation program aimed to promote alternative muscle recruitments could reduce the loads on the lower-limb joints during walking. The extent of joint load reduction one could expect from this approach remains unknown. Furthermore, assuming significant reductions of the load on the affected joint can be achieved, it is unclear whether, and to what extent, the other joints will be overloaded. Using subject-specific musculoskeletal models of four different participants, we computed the muscle recruitment strategies that minimised the hip, knee and ankle contact force, and predicted the contact forces such strategies induced at the other joints. Significant reductions of the peak force and impulse at the knee and hip were obtained, while only a minimal effect was found at the ankle joint. Adversely, the peak force and the impulse in non-targeted joints increased when aiming to minimize the load in an adjacent joint. These results confirm the potential of alternative muscle recruitment strategies to reduce the loading at the knee and the hip, but not at the ankle. Therefore, neuromuscular rehabilitation can be targeted to reduce the loading at affected joints but must be considered carefully in patients with multiple joints affected due to the potential adverse effects in non-targeted joints