Stochastic modelling of muscle recruitment during activity

Myobolica: A Stochastic Approach to Estimate Physiological Muscle Control Variability

The inherent redundancy of the musculoskeletal systems is traditionally solved by optimizing a cost function. This approach may not be correct to model non-adult or pathological populations likely to adopt a "non-optimal" motor control strategy. Over the years, various methods have been developed to address this limitation, such as the stochastic approach. A well-known implementation of this approach, Metabolica, samples a wide number of plausible solutions instead of searching for a single one, leveraging Bayesian statistics and Markov Chain Monte Carlo algorithm, yet allowing muscles to abruptly change their activation levels. To overcome this and other limitations, we developed a new implementation of the stochastic approach (Myobolica), adding constraints and parameters to ensure the identification of physiological solutions. The aim of this study was to evaluate Myobolica, and quantify the differences in terms of width of the solution band (muscle control variability) compared to Metabolica. To this end, both muscle forces and knee joint force solutions bands estimated by the two approaches were compared to one another, and against (i) the solution identified by static optimization and (ii) experimentally measured knee joint forces. The use of Myobolica led to a marked narrowing of the solution band compared to Metabolica. Furthermore, the Myobolica solutions well correlated with the experimental data (R 2 = 0.92 , RMSE = 0.3 BW), but not as much with the optimal solution (R 2 = 0.82 , RMSE = 0.63 BW). Additional analyses are required to confirm the findings and further improve this implementation.

Comparative evaluation of different spinal stability metrics

Direct in vivo measurements of spinal stability are not possible, leaving computational estimations (such as dynamic time series and structural analyses) as the feasible option. However, differences between different stability assessment approaches and metrics remain unclear. To explore this, we asked 32 participants to perform 35 cycles of repetitive lifts with and without load (4/2.6 kg for males/females). EMG signals and 3D kinematics were collected via 12 surface electrodes and 17 inertial sensors, and three dynamical stability measures were computed: short and long temporal and conventional maximum Lyapunov exponents (LyE) and maximum Floquet multipliers (FM). A dynamic subject-specific EMG-assisted musculoskeletal model computed four structural stability measures (critical muscle stiffness coefficient at which spine becomes unstable, average spine stiffness, minimum and geometric average of Hessian matrix eigenvalues). Across cycles, dynamical and structural stability outcomes varied noticeably. Temporal short-term LyE and all structural stability measures were more influenced by the cycle percentage (posture factor) than by phase (lifting, lowering) or load factor. The effect of all factors were non-significant for FM and long LyE, except for the posture on LyE-L with a small effect size. Pearson's correlations revealed a weak to moderate, or non-existent, correlation between structural and dynamical stability metrics, with small shared variances, underscoring their distinct and independent nature and theoretical foundations. Moreover, the low sensitivity of dynamic measures to posture and load factors, found in this study, calls for further examination. Considering the limitations and shortcomings of both dynamical and structural stability assessment approaches, there is a need for the development of improved musculoskeletal stability evaluation techniques.

EMG Validation of a Subject-Specific Thoracolumbar Spine Musculoskeletal Model During Dynamic Activities in Older Adults

Musculoskeletal models can uniquely estimate in vivo demands and injury risk. In this study, we aimed to compare muscle activations from subject-specific thoracolumbar spine OpenSim models with recorded muscle activity from electromyography (EMG) during five dynamic tasks. Specifically, 11 older adults (mean = 65 years, SD = 9) lifted a crate weighted to 10% of their body mass in axial rotation, 2-handed sagittal lift, 1-handed sagittal lift, and lateral bending, and simulated a window opening task. EMG measurements of back and abdominal muscles were directly compared to equivalent model-predicted activity for temporal similarity via maximum absolute normalized cross-correlation (MANCC) coefficients and for magnitude differences via root-mean-square errors (RMSE), across all combinations of participants, dynamic tasks, and muscle groups. We found that across most of the tasks the model reasonably predicted temporal behavior of back extensor muscles (median MANCC = 0.92 ± 0.07) but moderate temporal similarity was observed for abdominal muscles (median MANCC = 0.60 ± 0.20). Activation magnitude was comparable to previous modeling studies, and median RMSE was 0.18 ± 0.08 for back extensor muscles. Overall, these results indicate that our thoracolumbar spine model can be used to estimate subject-specific in vivo muscular activations for these dynamic lifting tasks.

A machine learning approach to quantify individual gait responses to ankle exoskeletons

Predicting an individual's response to an exoskeleton and understanding what data are needed to characterize responses remains challenging. Specifically, we lack a theoretical framework capable of quantifying heterogeneous responses to exoskeleton interventions. We leverage a neural network-based discrepancy modeling framework to quantify complex changes in gait in response to passive ankle exoskeletons in nondisabled adults. Discrepancy modeling aims to resolve dynamical inconsistencies between model predictions and real-world measurements. Neural networks identified models of (i) Nominal gait, (ii) Exoskeleton (Exo) gait, and (iii) the Discrepancy (i.e., response) between them. If an Augmented (Nominal+Discrepancy) model captured exoskeleton responses, its predictions should account for comparable amounts of variance in Exo gait data as the Exo model. Discrepancy modeling successfully quantified individuals' exoskeleton responses without requiring knowledge about physiological structure or motor control: a model of Nominal gait augmented with a Discrepancy model of response accounted for significantly more variance in Exo gait (median R2 for kinematics (0.928-0.963) and electromyography (0.665-0.788), (p<0.042)) than the Nominal model (median R2 for kinematics (0.863-0.939) and electromyography (0.516-0.664)). However, additional measurement modalities and/or improved resolution are needed to characterize Exo gait, as the discrepancy may not comprehensively capture response due to unexplained variance in Exo gait (median R2 for kinematics (0.954-0.977) and electromyography (0.724-0.815)). These techniques can be used to accelerate the discovery of individual-specific mechanisms driving exoskeleton responses, thus enabling personalized rehabilitation.

Neuromuscular control: from a biomechanist's perspective

Understanding neural control of movement necessitates a collaborative approach between many disciplines, including biomechanics, neuroscience, and motor control. Biomechanics grounds us to the laws of physics that our musculoskeletal system must obey. Neuroscience reveals the inner workings of our nervous system that functions to control our body. Motor control investigates the coordinated motor behaviours we display when interacting with our environment. The combined efforts across the many disciplines aimed at understanding human movement has resulted in a rich and rapidly growing body of literature overflowing with theories, models, and experimental paradigms. As a result, gathering knowledge and drawing connections between the overlapping but seemingly disparate fields can be an overwhelming endeavour. This review paper evolved as a need for us to learn of the diverse perspectives underlying current understanding of neuromuscular control. The purpose of our review paper is to integrate ideas from biomechanics, neuroscience, and motor control to better understand how we voluntarily control our muscles. As biomechanists, we approach this paper starting from a biomechanical modelling framework. We first define the theoretical solutions (i.e., muscle activity patterns) that an individual could feasibly use to complete a motor task. The theoretical solutions will be compared to experimental findings and reveal that individuals display structured muscle activity patterns that do not span the entire theoretical solution space. Prevalent neuromuscular control theories will be discussed in length, highlighting optimality, probabilistic principles, and neuromechanical constraints, that may guide individuals to families of muscle activity solutions within what is theoretically possible. Our intention is for this paper to serve as a primer for the neuromuscular control scientific community by introducing and integrating many of the ideas common across disciplines today, as well as inspire future work to improve the representation of neural control in biomechanical models.

Latarjet's muscular alterations increase glenohumeral joint stability: A theoretical study

The surgical Latarjet procedure aims to stabilise the glenohumeral joint following anterior dislocations. Despite restoring joint stability, the procedure introduces alterations of muscle paths which likely modify the shoulder dynamics. Currently, these altered muscular functions and their implications are unclear. Hence, this work aims to predict changes in muscle lever arms, muscle and joint forces following a Latarjet procedure by using a computational approach. Planar shoulder movements of ten participants were experimentally assessed. A validated upper-limb musculoskeletal model was utilised in two configurations, i.e., a baseline model, simulating normal joint, and a Latarjet model simulating its related muscular alterations. Muscle lever arms and differences in muscle and joint forces between models were derived from the experimental marker data and static optimisation technique. Lever arms of most altered muscles, hence their role, were substantially changed after Latarjet. Altered muscle forces varied by up to 15% of the body weight. Total glenohumeral joint force increased by up to 14% of the body weight after Latarjet, mostly due to increase in compression force. Our simulation indicated that the Latarjet muscular alterations lead to changes in the muscular recruitment and contribute to the stability of the glenohumeral joint by increasing compression force during planar motions.

A machine learning approach to quantify individual gait responses to ankle exoskeletons

We currently lack a theoretical framework capable of characterizing heterogeneous responses to exoskeleton interventions. Predicting an individual's response to an exoskeleton and understanding what data are needed to characterize responses has been a persistent challenge. In this study, we leverage a neural network-based discrepancy modeling framework to quantify complex changes in gait in response to passive ankle exoskeletons in nondisabled adults. Discrepancy modeling aims to resolve dynamical inconsistencies between model predictions and real-world measurements. Neural networks identified models of (i) Nominal gait, (ii) Exoskeleton ( Exo ) gait, and (iii) the Discrepancy ( i.e. , response) between them. If an Augmented (Nominal+Discrepancy) model captured exoskeleton responses, its predictions should account for comparable amounts of variance in Exo gait data as the Exo model. Discrepancy modeling successfully quantified individuals' exoskeleton responses without requiring knowledge about physiological structure or motor control: a model of Nominal gait augmented with a Discrepancy model of response accounted for significantly more variance in Exo gait (median R 2 for kinematics (0.928 - 0.963) and electromyography (0.665 - 0.788), ( p < 0.042)) than the Nominal model (median R 2 for kinematics (0.863 - 0.939) and electromyography (0.516 - 0.664)). However, additional measurement modalities and/or improved resolution are needed to characterize Exo gait, as the discrepancy may not comprehensively capture response due to unexplained variance in Exo gait (median R 2 for kinematics (0.954 - 0.977) and electromyography (0.724 - 0.815)). These techniques can be used to accelerate the discovery of individual-specific mechanisms driving exoskeleton responses, thus enabling personalized rehabilitation.

Using Bayesian inference to estimate plausible muscle forces in musculoskeletal models

Background

Musculoskeletal modeling is currently a preferred method for estimating the muscle forces that underlie observed movements. However, these estimates are sensitive to a variety of assumptions and uncertainties, which creates difficulty when trying to interpret the muscle forces from musculoskeletal simulations. Here, we describe an approach that uses Bayesian inference to identify plausible ranges of muscle forces for a simple motion while representing uncertainty in the measurement of the motion and the objective function used to solve the muscle redundancy problem.

Methods

We generated a reference elbow flexion–extension motion and computed a set of reference forces that would produce the motion while minimizing muscle excitations cubed via OpenSim Moco. We then used a Markov Chain Monte Carlo (MCMC) algorithm to sample from a posterior probability distribution of muscle excitations that would result in the reference elbow motion. We constructed a prior over the excitation parameters which down-weighted regions of the parameter space with greater muscle excitations. We used muscle excitations to find the corresponding kinematics using OpenSim, where the error in position and velocity trajectories (likelihood function) was combined with the sum of the cubed muscle excitations integrated over time (prior function) to compute the posterior probability density.

Results

We evaluated the muscle forces that resulted from the set of excitations that were visited in the MCMC chain (seven parallel chains, 500,000 iterations per chain). The estimated muscle forces compared favorably with the reference forces generated with OpenSim Moco, while the elbow angle and velocity from MCMC matched closely with the reference (average RMSE for elbow angle = 2°; and angular velocity = 32°/s). However, our rank plot analyses and potential scale reduction statistics, which we used to evaluate convergence of the algorithm, indicated that the chains did not fully mix.

Conclusions

While the results from this process are a promising step towards characterizing uncertainty in muscle force estimation, the computational time required to search the solution space with, and the lack of MCMC convergence indicates that further developments in MCMC algorithms are necessary for this process to become feasible for larger-scale models.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12984-022-01008-4.

Electroplating of HAp-brushite coating on metallic bioimplants with advanced hemocompatibility and osteocompatibility properties

In cases of severe bone tissue injuries, the use of metallic bioimplants is quite widespread due to their high strength, high fracture toughness, hardness, and corrosion resistance. However, they lack adequate biocompatibility and show poor metal-tissue integration during the post-operative phase. To mitigate this drawback, it is beneficial to add a biocompatible polymer layer to ensure a quick growth of cell or tissue over the surface of metallic bioimplant material. Furthermore, this additional layer should possess good adherence with the underlying material and also accompany a rapid bonding between the tissue and the implant material, in order to reduce the recovery time for the patient. Therefore, in this work, we report a novel green electroplating route for growing porous hydroxyapatite-brushite coatings on a stainless steel surface. The malic acid used for the production of hydroxyapatite-brushite coatings has been obtained from an extract of locally available apple fruit (Malus domestica). We demonstrate the effect of electroplating parameters on the structural morphology of the electroplated composite layer via XRD, SEM with EDS, and FTIR characterization techniques and report an optimized set of electroplating parameters that will yield the best composite coating in terms of thickness, adherence to substrate and speed. The hemocompatibility and osteocompatibility studies on the electroplated composites coating show this technology's effectiveness and potential applicability in biomedical applications. Compared to other routes reported in the literature, this electroplating route is quicker and yields better composite coatings with faster bone tissue growth potential.

Real-time replication of three-dimensional and time-varying physiological loading cycles for bone and implant testing: A novel protocol demonstrated for the proximal human femur while walking

In vitro real-time replication of three-dimensional, time-varying load profiles acting on human bones during physical activity can advance bone and implant testing protocols. This study aimed to develop a novel protocol for applying the three-dimensional, time-varying hip contact force while walking to a human femur specimen. The target force profile was obtained from the literature. A proximal femur from an elderly female donor was instrumented using ten rosette strain gages and tested using a custom-made hexapod robot. A load-control algorithm determined the robot position generating the target force at low frequency (0.0004 Hz). Five cycles of the robot position were played back at five intermediate frequencies up to real-time (0.04, 0.08, 0.16, 0.4, and 0.8 Hz). The hip reaction force, the length of the actuators (position), and cortical strains were compared. The error in the load-control force was 0.3 ± 4.2 N (mean ± SD). The last three force, position, and strain cycles varied by less than 1.1% for every frequency analyzed. Across frequencies, the force increased by 28% at 0.8 Hz as a logarithmic function of frequency (R² = 0.98). The position and strain error linearly increased with frequency up to 0.4 Hz. The median position error and the interquartile range of the strain error reached 15% and 13% at 0.8 Hz. Changes of force and cortical strain at increasing frequencies were linearly related (R² = 0.99). Therefore, the protocol developed can provide repeatable three-dimensional time-varying load profiles, although the comparison of the specimen deformation obtained across frequencies should be considered with care, particularly in the higher frequency range. This information supports the design of dynamic tests of bone and implants.

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

Background:

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

Purpose:

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

Study Design:

Descriptive laboratory study.

Methods:

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

Results:

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

Conclusion:

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

Clinical Relevance:

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

A Systematic Review of the Associations Between Inverse Dynamics and Musculoskeletal Modeling to Investigate Joint Loading in a Clinical Environment

The assessment of knee or hip joint loading by external joint moments is mainly used to draw conclusions on clinical decision making. However, the correlation between internal and external loads has not been systematically analyzed. This systematic review aims, therefore, to clarify the relationship between external and internal joint loading measures during gait. A systematic database search was performed to identify appropriate studies for inclusion. In total, 4,554 articles were identified, while 17 articles were finally included in data extraction. External joint loading parameters were calculated using the inverse dynamics approach and internal joint loading parameters by musculoskeletal modeling or instrumented prosthesis. It was found that the medial and total knee joint contact forces as well as hip joint contact forces in the first half of stance can be well predicted using external joint moments in the frontal plane, which is further improved by including the sagittal joint moment. Worse correlations were found for the peak in the second half of stance as well as for internal lateral knee joint contact forces. The estimation of external joint moments is useful for a general statement about the peak in the first half of stance or for the maximal loading. Nevertheless, when investigating diseases as valgus malalignment, the estimation of lateral knee joint contact forces is necessary for clinical decision making because external joint moments could not predict the lateral knee joint loading sufficient enough. Dependent on the clinical question, either estimating the external joint moments by inverse dynamics or internal joint contact forces by musculoskeletal modeling should be used.

Human motor control: Is a subject-specific quantitative assessment of its multiple characteristics possible? A demonstrative application on children motor development

In synergy with the musculoskeletal system, motor control is responsible of motor performance, determining joint kinematics and kinetics as related to task and environmental constraints. Multiple metrics have been proposed to quantify motor control from kinematic measures of motion, each index quantifying a different specific aspect, but the characterization of motor control as related to a specific subject or population during the execution of a specific task is still missing. In the present work, the performance of a novel approach for quantitative parametrization of motor control is tested over 86 primary school children: 36 I grade, 50 II grade; 40 females, 46 males. Children were assessed performing natural and tandem gait using 3 inertial measurement units, and gait variability, regularity, and complexity indexes were calculated from gait temporal parameters and trunk acceleration. Standard Test of Motor Competence and Developmental Coordination Disorder Questionnaire were used to assess reference motor competence. The proposed set of parameters allowed to discriminate the level of motor competence as related to age and standardised scales, while differences related to sex resulted negligible. The proposed method can effectively integrate musculoskeletal dynamic models, allowing the parametric characterization of motor control of specific subjects and/or populations.

Predicting walking response to ankle exoskeletons using data-driven models

Despite recent innovations in exoskeleton design and control, predicting subject-specific impacts of exoskeletons on gait remains challenging. We evaluated the ability of three classes of subject-specific phase-varying (PV) models to predict kinematic and myoelectric responses to ankle exoskeletons during walking, without requiring prior knowledge of specific user characteristics. Each model-PV, linear PV (LPV) and nonlinear PV (NPV)-leveraged Floquet theory to predict deviations from a nominal gait cycle due to exoskeleton torque, though the models differed in complexity and expected prediction accuracy. For 12 unimpaired adults walking with bilateral passive ankle exoskeletons, we predicted kinematics and muscle activity in response to three exoskeleton torque conditions. The LPV model's predictions were more accurate than the PV model when predicting less than 12.5% of a stride in the future and explained 49-70% of the variance in hip, knee and ankle kinematic responses to torque. The LPV model also predicted kinematic responses with similar accuracy to the more-complex NPV model. Myoelectric responses were challenging to predict with all models, explaining at most 10% of the variance in responses. This work highlights the potential of data-driven PV models to predict complex subject-specific responses to ankle exoskeletons and inform device design and control.

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.

Population-Based Bone Strain During Physical Activity: A Novel Method Demonstrated for the Human Femur

Statistical methods are increasingly used in biomechanics for studying bone geometry, bone density distribution and function in the population. However, relating population-based bone variation to strain during activity is computationally challenging. Here, we describe a new method for calculating strain in a population, using the Superposition Principle Method Squared (SPM²), and we demonstrate the method for calculating strain in human femurs. Computed-tomography images and motion capture while walking in 21 healthy adult women were obtained earlier. Variation of femur geometry and bone distribution were modelled using active shape and appearance modelling (ASAM). Femoral strain was modelled as the weighted sum of strain generated by each force in the model plus a strain variation assumed a quadratic function of the ASAM scores. The quadratic coefficients were fitted to 35 instances drawn from the ASAM model by varying each eigenmode by ± 2 SD. The equivalent strain in matched finite-element and SPM² calculations was obtained for 40 frames of walking for three independent cases and 50 ASAM instances. Finite-element and SPM2 solutions for walking were obtained in 44 and 3 min respectively. The SPM2 model accurately predicted strain for the three independent instances (R-squared 0.83-0.94) and the 50 ASAM instances (R-squared 0.95-1.00). The method developed enables fast and accurate calculation of population-based femoral strain.

Knee abduction moment is predicted by lower gluteus medius force and larger vertical and lateral ground reaction forces during drop vertical jump in female athletes

Prospective knee abduction moments measured during the drop vertical jump task identify those at increased risk for anterior cruciate ligament injury. The purpose of this study was to determine which muscle forces and frontal plane biomechanical features contribute to large knee abduction moments. Thirteen young female athletes performed three drop vertical jump trials. Subject-specific musculoskeletal models and electromyography-informed simulations were developed to calculate the frontal plane biomechanics and lower limb muscle forces. The relationships between knee abduction moment and frontal plane biomechanics were examined. Knee abduction moment was positively correlated to vertical (R = 0.522, P < 0.001) and lateral ground reaction forces (R = 0.395, P = 0.016), hip adduction angle (R = 0.358, P < 0.023) and lateral pelvic tilt (R = 0.311, P = 0.061). A multiple regression showed that knee abduction moment was predicted by reduced gluteus medius force and increased vertical and lateral ground reaction forces (P < 0.001, R² = 0.640). Hip adduction is indicative of lateral pelvic shift during landing. The coupled hip adduction and lateral pelvic tilt were associated to the increased vertical and lateral ground reaction forces, propagating into higher knee abduction moments. These biomechanical features are associated with ACL injury and may be limited in a landing with increased activation of the gluteus medius. Targeted neuromuscular training to control the frontal pelvic and hip motion may help to avoid injurious ground reaction forces and consequent knee abduction moment and ACL injury risk.

Connecting the legs with a spring improves human running economy

Human running is inefficient. For every 10 calories burned, less than 1 is needed to maintain a constant forward velocity - the remaining energy is, in a sense, wasted. The majority of this wasted energy is expended to support the bodyweight and redirect the center of mass during the stance phase of gait. An order of magnitude less energy is expended to brake and accelerate the swinging leg. Accordingly, most devices designed to increase running efficiency have targeted the costlier stance phase of gait. An alternative approach is seen in nature: spring-like tissues in some animals and humans are believed to assist leg swing. While it has been assumed that such a spring simply offloads the muscles that swing the legs, thus saving energy, this mechanism has not been experimentally investigated. Here, we show that a spring, or 'exotendon', connecting the legs of a human reduces the energy required for running by 6.4±2.8%, and does so through a complex mechanism that produces savings beyond those associated with leg swing. The exotendon applies assistive forces to the swinging legs, increasing the energy optimal stride frequency. Runners then adopt this frequency, taking faster and shorter strides, and reduce the joint mechanical work to redirect their center of mass. Our study shows how a simple spring improves running economy through a complex interaction between the changing dynamics of the body and the adaptive strategies of the runner, highlighting the importance of considering each when designing systems that couple human and machine.

EMG-Informed Musculoskeletal Modeling to Estimate Realistic Knee Anterior Shear Force During Drop Vertical Jump in Female Athletes

The anterior cruciate ligament is the primary structural restraint to tibial anterior shear force. The anterior force occurring at the knee during landing contributes to anterior cruciate ligament injury risk, but it cannot be directly measured experimentally. The objective of this study was to develop electromyography-informed musculoskeletal simulations of the drop vertical jump motor task and assess the contribution of knee muscle forces to tibial anterior shear force. In this cross-sectional study, musculoskeletal simulations were used to estimate the muscle forces of thirteen female athletes performing a drop vertical jump using an electromyography-informed method. Muscle activation and knee loads that resulted from these simulations were compared to the results obtained with the more common approach of minimization of muscle effort (optimization-based method). Quadriceps-hamstrings and quadriceps-gastrocnemius co-contractions were progressively increased and their contribution to anterior shear force was quantified. The electromyography-informed method produced co-contraction indexes more consistent with electromyography data than the optimization-based method. The muscles that presented the largest contribution to peak anterior shear force were the gastrocnemii, likely from their wrapping around the posterior aspect of the tibia. The quadriceps-hamstring co-contraction provided a protective effect on the ACL and reduced peak anterior shear force by 292 N with a co-contraction index increase of 25% from baseline (31%), whereas a quadriceps-gastrocnemius co-contraction index of 61% increased peak anterior shear force by 797 N compared to baseline (42%). An increase in gastrocnemius contraction, which might be required to protect the ankle from the impact with the ground, produced a large quadriceps-gastrocnemius co-activation, increasing peak anterior shear force. A better understanding of each muscle's contribution to anterior shear force and, consequently, anterior cruciate ligament tension may inform subject-specific injury prevention programs and rehabilitation protocols.

Do bone geometric properties of the proximal femoral diaphysis reflect loading history, muscle properties, or body dimensions?

OBJECTIVES

The aim of this study was to investigate activity-induced effects from bone geometric properties of the proximal femur in athletic vs nonathletic healthy females by statistically controlling for variation in body size, lower limb isometric, and dynamic muscle strength, and cross-sectional area of Musculus gluteus maximus.

METHODS

The material consists of hip and proximal thigh magnetic resonance images of Finnish female athletes (N = 91) engaged in either high jump, triple jump, soccer, squash, powerlifting, endurance running or swimming, and a group of physically active nonathletic women (N = 20). Cross-sectional bone geometric properties were calculated for the lesser trochanter, sub-trochanter, and mid-shaft of the femur regions. Bone geometric properties were analyzed using a general linear model that included body size, muscle size, and muscle strength as covariates.

RESULTS

Body size and isometric muscle strength were positively associated with bone geometric properties at all three cross-sectional levels of the femur, while muscle size was positively associated with bone properties only at the femur mid-shaft. When athletes were compared to nonathletic females, triple jump, soccer, and squash resulted in greater values in all studied cross-sections; high jump and endurance running resulted in greater values at the femoral mid-shaft cross-section; and swimming resulted in lower values at sub-trochanter and femur mid-shaft cross-sections.

CONCLUSIONS

Activity effects from ground impact loading were associated with higher bone geometric values, especially at the femur mid-shaft, but also at lesser and sub-trochanter cross-sections. Bone geometric properties along the femur can be used to assess the mechanical stimuli experienced, where ground impact loading seems to be more important than muscle loading.

A novel training-free method for real-time prediction of femoral strain

Surrogate methods for rapid calculation of femoral strain are limited by the scope of the training data. We compared a newly developed training-free method based on the superposition principle (Superposition Principle Method, SPM) and popular surrogate methods for calculating femoral strain during activity. Finite-element calculations of femoral strain, muscle, and joint forces for five different activity types were obtained previously. Multi-linear regression, multivariate adaptive regression splines, and Gaussian process were trained for 50, 100, 200, and 300 random samples generated using Latin Hypercube (LH) and Design of Experiment (DOE) sampling. The SPM method used weighted linear combinations of 173 activity-independent finite-element analyses accounting for each muscle and hip contact force. Across the surrogate methods, we found that 200 DOE samples consistently provided low error (RMSE < 100 µε), with model construction time ranging from 3.8 to 63.3 h and prediction time ranging from 6 to 1236 s per activity. The SPM method provided the lowest error (RMSE = 40 µε), the fastest model construction time (3.2 h) and the second fastest prediction time per activity (36 s) after Multi-linear Regression (6 s). The SPM method will enable large numerical studies of femoral strain and will narrow the gap between bone strain prediction and real-time clinical applications.

A computationally efficient strategy to estimate muscle forces in a finite element musculoskeletal model of the lower limb

Concurrent multiscale simulation strategies are required in computational biomechanics to study the interdependence between body scales. However, detailed finite element models rarely include muscle recruitment due to the computational burden of both the finite element method and the optimization strategies widely used to estimate muscle forces. The aim of this study was twofold: first, to develop a computationally efficient muscle force prediction strategy based on proportional-integral-derivative (PID) controllers to track gait and chair rise experimental joint motion with a finite element musculoskeletal model of the lower limb, including a deformable knee representation with 12 degrees of freedom; and, second, to demonstrate that the inclusion of joint-level deformability affects muscle force estimation by using two different knee models and comparing muscle forces between the two solutions. The PID control strategy tracked experimental hip, knee, and ankle flexion/extension with root mean square errors below 1°, and estimated muscle, contact and ligament forces in good agreement with previous results and electromyography signals. Differences up to 11% and 20% in the vasti and biceps femoris forces, respectively, were observed between the two knee models, which might be attributed to a combination of differing joint contact geometry, ligament behavior, joint kinematics, and muscle moment arms. The tracking strategy developed in this study addressed the inevitable tradeoff between computational cost and model detail in musculoskeletal simulations and can be used with finite element musculoskeletal models to efficiently estimate the interdependence between muscle forces and tissue deformation.

Gait Rehabilitation Using Functional Electrical Stimulation Induces Changes in Ankle Muscle Coordination in Stroke Survivors: A Preliminary Study

Background: Previous studies have demonstrated that post-stroke gait rehabilitation combining functional electrical stimulation (FES) applied to the ankle muscles during fast treadmill walking (FastFES) improves gait biomechanics and clinical walking function. However, there is considerable inter-individual variability in response to FastFES. Although FastFES aims to sculpt ankle muscle coordination, whether changes in ankle muscle activity underlie observed gait improvements is unknown. The aim of this study was to investigate three cases illustrating how FastFES modulates ankle muscle recruitment during walking.

Methods: We conducted a preliminary case series study on three individuals (53–70 y; 2 M; 35–60 months post-stroke; 19–22 lower extremity Fugl-Meyer) who participated in 18 sessions of FastFES (3 sessions/week; ClinicalTrials.gov: NCT01668602). Clinical walking function (speed, 6-min walk test, and Timed-Up-and-Go test), gait biomechanics (paretic propulsion and ankle angle at initial-contact), and plantarflexor (soleus)/dorsiflexor (tibialis anterior) muscle recruitment were assessed pre- and post-FastFES while walking without stimulation.

Results:Two participants (R1, R2) were categorized as responders based on improvements in clinical walking function. Consistent with heterogeneity of clinical and biomechanical changes commonly observed following gait rehabilitation, how muscle activity was altered with FastFES differed between responders. R1 exhibited improved plantarflexor recruitment during stance accompanied by increased paretic propulsion. R2 exhibited improved dorsiflexor recruitment during swing accompanied by improved paretic ankle angle at initial-contact. In contrast, the third participant (NR1), classified as a non-responder, demonstrated increased ankle muscle activity during inappropriate phases of the gait cycle. Across all participants, there was a positive relationship between increased walking speeds after FastFES and reduced SOL/TA muscle coactivation.

Conclusion:Our preliminary case series study is the first to demonstrate that improvements in ankle plantarflexor and dorsiflexor muscle recruitment (muscles targeted by FastFES) accompanied improvements in gait biomechanics and walking function following FastFES in individuals post-stroke. Our results also suggest that inducing more appropriate (i.e., reduced) ankle plantar/dorsi-flexor muscle coactivation may be an important neuromuscular mechanism underlying improvements in gait function after FastFES training, suggesting that pre-treatment ankle muscle status could be used for inclusion into FastFES. The findings of this case-series study, albeit preliminary, provide the rationale and foundations for larger-sample studies using similar methodology.

Efficacy and efficiency of multivariate linear regression for rapid prediction of femoral strain fields during activity

Multivariate Linear Regression-based (MLR) surrogate models were explored to reduce the computational cost of predicting femoral strains during normal activity in comparison with finite element analysis. The musculoskeletal model of one individual, the finite-element model of the right femur, and experimental force and motion data for normal walking, fast walking, stair ascent, stair descent, and rising from a chair were obtained from a previous study. Equivalent Von Mises strain was calculated for 1000 frames uniformly distributed across activities. MLR surrogate models were generated using training sets of 50, 100, 200 and 300 samples. The finite-element and MLR analyses were compared using linear regression. The Root Mean Square Error (RMSE) and the 95th percentile of the strain error distribution were used as indicators of average and peak error. The MLR model trained using 200 samples (RMSE < 108 µε; peak error < 228 µε) was used as a reference. The finite-element method required 66 s per frame on a standard desktop computer. The MLR model required 0.1 s per frame plus 1848 s of training time. RMSE ranged from 1.2% to 1.3% while peak error ranged from 2.2% to 3.6% of the maximum micro-strain (5020 µε). Performance within an activity was lower during early and late stance, with RMSE of 4.1% and peak error of 8.6% of the maximum computed micro-strain. These results show that MLR surrogate models may be used to rapidly and accurately estimate strain fields in long bones during daily physical activity.

In silico assessment of biomedical products: The conundrum of rare but not so rare events in two case studies

In silico clinical trials, defined as "The use of individualized computer simulation in the development or regulatory evaluation of a medicinal product, medical device, or medical intervention," have been proposed as a possible strategy to reduce the regulatory costs of innovation and the time to market for biomedical products. We review some of the the literature on this topic, focusing in particular on those applications where the current practice is recognized as inadequate, as for example, the detection of unexpected severe adverse events too rare to be detected in a clinical trial, but still likely enough to be of concern. We then describe with more details two case studies, two successful applications of in silico clinical trial approaches, one relative to the University of Virginia/Padova simulator that the Food and Drug Administration has accepted as possible replacement for animal testing in the preclinical assessment of artificial pancreas technologies, and the second, an investigation of the probability of cardiac lead fracture, where a Bayesian network was used to combine in vivo and in silico observations, suggesting a whole new strategy of in silico-augmented clinical trials, to be used to increase the numerosity where recruitment is impossible, or to explore patients' phenotypes that are unlikely to appear in the trial cohort, but are still frequent enough to be of concern.

In Vivo Neuromechanics: Decoding Causal Motor Neuron Behavior with Resulting Musculoskeletal Function

Human motor function emerges from the interaction between the neuromuscular and the musculoskeletal systems. Despite the knowledge of the mechanisms underlying neural and mechanical functions, there is no relevant understanding of the neuro-mechanical interplay in the neuro-musculo-skeletal system. This currently represents the major challenge to the understanding of human movement. We address this challenge by proposing a paradigm for investigating spinal motor neuron contribution to skeletal joint mechanical function in the intact human in vivo. We employ multi-muscle spatial sampling and deconvolution of high-density fiber electrical activity to decode accurate α-motor neuron discharges across five lumbosacral segments in the human spinal cord. We use complete α-motor neuron discharge series to drive forward subject-specific models of the musculoskeletal system in open-loop with no corrective feedback. We perform validation tests where mechanical moments are estimated with no knowledge of reference data over unseen conditions. This enables accurate blinded estimation of ankle function purely from motor neuron information. Remarkably, this enables observing causal associations between spinal motor neuron activity and joint moment control. We provide a new class of neural data-driven musculoskeletal modeling formulations for bridging between movement neural and mechanical levels in vivo with implications for understanding motor physiology, pathology, and recovery.

The Virtual Physiological Human: Ten Years After

Biomedical research and clinical practice are struggling to cope with the growing complexity that the progress of health care involves. The most challenging diseases, those with the largest socioeconomic impact (cardiovascular conditions; musculoskeletal conditions; cancer; metabolic, immunity, and neurodegenerative conditions), are all characterized by a complex genotype-phenotype interaction and by a "systemic" nature that poses a challenge to the traditional reductionist approach. In 2005 a small group of researchers discussed how the vision of computational physiology promoted by the Physiome Project could be translated into clinical practice and formally proposed the term Virtual Physiological Human. Our knowledge about these diseases is fragmentary, as it is associated with molecular and cellular processes on the one hand and with tissue and organ phenotype changes (related to clinical symptoms of disease conditions) on the other. The problem could be solved if we could capture all these fragments of knowledge into predictive models and then compose them into hypermodels that help us tame the complexity that such systemic behavior involves. In 2005 this was simply not possible-the necessary methods and technologies were not available. Now, 10 years later, it seems the right time to reflect on the original vision, the results achieved so far, and what remains to be done.

Hammering Does Not Fit Fitts' Law

While movement is essential to human wellbeing, we are still unable to reproduce the deftness and robustness of human movement in automatons or completely restore function to individuals with many types of motor impairment. To better understand how the human nervous system plans and controls movements, neuromechanists employ simple tasks such as upper extremity reaches and isometric force tasks. However, these simple tasks rarely consider impacts and may not capture aspects of motor control that arise from real-world complexity. Here we compared existing models of motor control with the results of a periodic targeted impact task extended from Bernstein's seminal work: hammering a nail into wood. We recorded impact forces and kinematics from 10 subjects hammering at different frequencies and with hammers with different physical properties (mass and face area). We found few statistical differences in most measures between different types of hammer, demonstrating human robustness to minor changes in dynamics. Because human motor control is thought to obey optimality principles, we also developed a feedforward optimal simulation with a neuromechanically inspired cost function that reproduces the experimental data. However, Fitts' Law, which relates movement time to distance traveled and target size, did not match our experimental data. We therefore propose a new model in which the distance moved is a logarithmic function of the time to move that yields better results (R2 ≥ 0.99 compared to R2 ≥ 0.88). These results support the argument that humans control movement in an optimal way, but suggest that Fitts' Law may not generalize to periodic impact tasks.

Prediction of In Vivo Knee Joint Loads Using a Global Probabilistic Analysis

Musculoskeletal models are powerful tools that allow biomechanical investigations and predictions of muscle forces not accessible with experiments. A core challenge modelers must confront is validation. Measurements of muscle activity and joint loading are used for qualitative and indirect validation of muscle force predictions. Subject-specific models have reached high levels of complexity and can predict contact loads with surprising accuracy. However, every deterministic musculoskeletal model contains an intrinsic uncertainty due to the high number of parameters not identifiable in vivo. The objective of this work is to test the impact of intrinsic uncertainty in a scaled-generic model on estimates of muscle and joint loads. Uncertainties in marker placement, limb coronal alignment, body segment parameters, Hill-type muscle parameters, and muscle geometry were modeled with a global probabilistic approach (multiple uncertainties included in a single analysis). 5-95% confidence bounds and input/output sensitivities of predicted knee compressive loads and varus/valgus contact moments were estimated for a gait activity of three subjects with telemetric knee implants from the "Grand Challenge Competition." Compressive load predicted for the three subjects showed confidence bounds of 333 ± 248 N, 408 ± 333 N, and 379 ± 244 N when all the sources of uncertainty were included. The measured loads lay inside the predicted 5-95% confidence bounds for 77%, 83%, and 76% of the stance phase. Muscle maximum isometric force, muscle geometry, and marker placement uncertainty most impacted the joint load results. This study demonstrated that identification of these parameters is crucial when subject-specific models are developed.

Suboptimal Muscle Synergy Activation Patterns Generalize their Motor Function across Postures

We used a musculoskeletal model to investigate the possible biomechanical and neural bases of using consistent muscle synergy patterns to produce functional motor outputs across different biomechanical conditions, which we define as generalizability. Experimental studies in cats demonstrate that the same muscle synergies are used during reactive postural responses at widely varying configurations, producing similarly-oriented endpoint force vectors with respect to the limb axis. However, whether generalizability across postures arises due to similar biomechanical properties or to neural selection of a particular muscle activation pattern has not been explicitly tested. Here, we used a detailed cat hindlimb model to explore the set of feasible muscle activation patterns that produce experimental synergy force vectors at a target posture, and tested their generalizability by applying them to different test postures. We used three methods to select candidate muscle activation patterns: (1) randomly-selected feasible muscle activation patterns, (2) optimal muscle activation patterns minimizing muscle effort at a given posture, and (3) generalizable muscle activation patterns that explicitly minimized deviations from experimentally-identified synergy force vectors across all postures. Generalizability was measured by the deviation between the simulated force direction of the candidate muscle activation pattern and the experimental synergy force vectors at the test postures. Force angle deviations were the greatest for the randomly selected feasible muscle activation patterns (e.g., >100°), intermediate for effort-wise optimal muscle activation patterns (e.g., ~20°), and smallest for generalizable muscle activation patterns (e.g., <5°). Generalizable muscle activation patterns were suboptimal in terms of effort, often exceeding 50% of the maximum possible effort (cf. ~5% in minimum-effort muscle activation patterns). The feasible muscle activation ranges of individual muscles associated with producing a specific synergy force vector was reduced by ~45% when generalizability requirements were imposed. Muscles recruited in the generalizable muscle activation patterns had less sensitive torque-producing characteristics to changes in postures. We conclude that generalization of function across postures does not arise from limb biomechanics or a single optimality criterion. Muscle synergies may reflect acquired motor solutions globally tuned for generalizability across biomechanical contexts, facilitating rapid motor adaptation.

Feasible muscle activation ranges based on inverse dynamics analyses of human walking

Although it is possible to produce the same movement using an infinite number of different muscle activation patterns owing to musculoskeletal redundancy, the degree to which observed variations in muscle activity can deviate from optimal solutions computed from biomechanical models is not known. Here, we examined the range of biomechanically permitted activation levels in individual muscles during human walking using a detailed musculoskeletal model and experimentally-measured kinetics and kinematics. Feasible muscle activation ranges define the minimum and maximum possible level of each muscle's activation that satisfy inverse dynamics joint torques assuming that all other muscles can vary their activation as needed. During walking, 73% of the muscles had feasible muscle activation ranges that were greater than 95% of the total muscle activation range over more than 95% of the gait cycle, indicating that, individually, most muscles could be fully active or fully inactive while still satisfying inverse dynamics joint torques. Moreover, the shapes of the feasible muscle activation ranges did not resemble previously-reported muscle activation patterns nor optimal solutions, i.e. static optimization and computed muscle control, that are based on the same biomechanical constraints. Our results demonstrate that joint torque requirements from standard inverse dynamics calculations are insufficient to define the activation of individual muscles during walking in healthy individuals. Identifying feasible muscle activation ranges may be an effective way to evaluate the impact of additional biomechanical and/or neural constraints on possible versus actual muscle activity in both normal and impaired movements.

Modeling and simulating the neuromuscular mechanisms regulating ankle and knee joint stiffness during human locomotion

This work presents an electrophysiologically and dynamically consistent musculoskeletal model to predict stiffness in the human ankle and knee joints as derived from the joints constituent biological tissues (i.e., the spanning musculotendon units). The modeling method we propose uses electromyography (EMG) recordings from 13 muscle groups to drive forward dynamic simulations of the human leg in five healthy subjects during overground walking and running. The EMG-driven musculoskeletal model estimates musculotendon and resulting joint stiffness that is consistent with experimental EMG data as well as with the experimental joint moments. This provides a framework that allows for the first time observing 1) the elastic interplay between the knee and ankle joints, 2) the individual muscle contribution to joint stiffness, and 3) the underlying co-contraction strategies. It provides a theoretical description of how stiffness modulates as a function of muscle activation, fiber contraction, and interacting tendon dynamics. Furthermore, it describes how this differs from currently available stiffness definitions, including quasi-stiffness and short-range stiffness. This work offers a theoretical and computational basis for describing and investigating the neuromuscular mechanisms underlying human locomotion.