Personalized neuromusculoskeletal (NMS) models can represent the neurological, physiological, and anatomical characteristics of an individual and can be used to estimate the forces generated inside the human body. Currently, publicly available software to calculate muscle forces are restricted to static and dynamic optimisation methods, or limited to isometric tasks only. We have created and made freely available for the research community the Calibrated EMG-Informed NMS Modelling Toolbox (CEINMS), an OpenSim plug-in that enables investigators to predict different neural control solutions for the same musculoskeletal geometry and measured movements. CEINMS comprises EMG-driven and EMG-informed algorithms that have been previously published and tested. It operates on dynamic skeletal models possessing any number of degrees of freedom and musculotendon units and can be calibrated to the individual to predict measured joint moments and EMG patterns. In this paper we describe the components of CEINMS and its integration with OpenSim. We then analyse how EMG-driven, EMG-assisted, and static optimisation neural control solutions affect the estimated joint moments, muscle forces, and muscle excitations, including muscle co-contraction.
|Pizzolato C., Lloyd D.G., Sartori M., Fregly B.J., Ceseracciu E., Besier T.F., Reggiani M., CEINMS: a toolbox to investigate the influence of different neural solutions on the prediction of muscle excitation and joint moments during dynamic motor tasks, Journal of Biomechanics, 48(14): 3929–3936, 2015. (2015) View|
|Gerus P., Sartori M., Besier T.F., Fregly B.J., Delp S.L., Banks S., Pandy M.G., D’Lima D, Lloyd D.G., Subject-specific knee-joint geometry improves predictions of medial tibiofemoral contact forces Journal of Biomechanics, 46(16): 2778-2786, 2013 (2013) View|
Estimating tibiofemoral joint contact forces is important for understanding the initiation and progression of knee osteoarthritis. However, tibiofemoral contact force predictions are influenced by many factors including muscle forces and anatomical representations of the knee joint. This study aimed to investigate the influence of subject-specific geometry and knee joint kinematics on the prediction of tibiofemoral contact forces using a calibrated EMG-driven neuromusculoskeletal model of the knee. One participant fitted with an instrumented total knee replacement walked at a self-selected speed while medial and lateral tibiofemoral contact forces, ground reaction forces, whole-body kinematics, and lower-limb muscle activity were simultaneously measured. The combination of generic and subject-specific knee joint geometry and kinematics resulted in four different OpenSim models used to estimate muscle-tendon lengths and moment arms. The subject-specific geometric model was created from CT scans and the subject-specific knee joint kinematics representing the translation of the tibia relative to the femur was obtained from fluoroscopy. The EMG-driven model was calibrated using one walking trial, but with three different cost functions that tracked the knee flexion/extension moments with and without constraint over the estimated joint contact forces. The calibrated models then predicted the medial and lateral tibiofemoral contact forces for five other different walking trials. The use of subject-specific models with minimization of the peak tibiofemoral contact forces improved the accuracy of medial contact forces by 47% and lateral contact forces by 7%, respectively compared with the use of generic musculoskeletal model.
|Lloyd D.G., Besier T.F., An EMG-driven musculoskeletal model for estimation of the human knee joint moments across varied tasks. Journal of Biomechanics, 36(6): 765-776, 2003. (2003) View|
This paper examined if an electromyography (EMG) driven musculoskeletal model of the human knee could be used to predict knee moments, calculated using inverse dynamics, across a varied range of dynamic contractile conditions. Muscle-tendon lengths and moment arms of 13 muscles crossing the knee joint were determined from joint kinematics using a three-dimensional anatomical model of the lower limb. Muscle activation was determined using a second-order discrete non-linear model using rectified and low-pass filtered EMG as input. A modified Hill-type muscle model was used to calculate individual muscle forces using activation and muscle tendon lengths as inputs. The model was calibrated to six individuals by altering a set of physiologically based parameters using mathematical optimisation to match the net flexion/extension (FE) muscle moment with those measured by inverse dynamics. The model was calibrated for each subject using 5 different tasks, including passive and active FE in an isokinetic dynamometer, running, and cutting manoeuvres recorded using three-dimensional motion analysis. Once calibrated, the model was used to predict the FE moments, estimated via inverse dynamics, from over 200 isokinetic dynamometer, running and sidestepping tasks. The inverse dynamics joint moments were predicted with an average R(2) of 0.91 and mean residual error of approximately 12 Nm. A re-calibration of only the EMG-to-activation parameters revealed FE moments prediction across weeks of similar accuracy. Changing the muscle model to one that is more physiologically correct produced better predictions. The modelling method presented represents a good way to estimate in vivo muscle forces during
|Sartori M., Reggiani, M., Pagello E., Lloyd D.G. Modeling the Human Knee for Assistive Technologies. IEEE Transactions on Biomedical Engineering, 59(9):2642-9, 2012. (2012) View|
In this paper, we use motion capture technology together with an EMG-driven musculoskeletal model of the knee joint to predict muscle behavior during human dynamic movements. We propose a muscle model based on infinitely stiff tendons and show this allows speeding up 250 times the computation of muscle force and the resulting joint moment calculation with no loss of accuracy with respect to the previously developed elastic-tendon model. We then integrate our previously developed method for the estimation of 3-D musculotendon kinematics in the proposed EMG-driven model. This new code enabled the creation of a standalone EMG-driven model that was implemented and run on an embedded system for applications in assistive technologies such as myoelectrically controlled prostheses and orthoses.
|Sartori M., Farina D., Lloyd D.G. Hybrid neuromusculoskeletal modeling best tracks joint moments using a balance between muscle-excitations derived from EMG and optimization. Journal of Biomechanics 47(15): 3613–3621, 2014 (2014) View|
Current electromyography (EMG)-driven musculoskeletal models are used to estimate joint moments measured from an individual׳s extremities during dynamic movement with varying levels of accuracy. The main benefit is the underlying musculoskeletal dynamics is simulated as a function of realistic, subject-specific, neural-excitation patterns provided by the EMG data. The main disadvantage is surface EMG cannot provide information on deeply located muscles. Furthermore, EMG data may be affected by cross-talk, recording and post-processing artifacts that could adversely influence the EMG׳s information content. This limits the EMG-driven model׳s ability to calculate the multi-muscle dynamics and the resulting joint moments about multiple degrees of freedom. We present a hybrid neuromusculoskeletal model that combines calibration, subject-specificity, EMG-driven and static optimization methods together. In this, the joint moment tracking errors are minimized by balancing the information content extracted from the experimental EMG data and from that generated by a static optimization method. Using movement data from five healthy male subjects during walking and running we explored the hybrid model׳s best configuration to minimally adjust recorded EMGs and predict missing EMGs while attaining the best tracking of joint moments. Minimally adjusted and predicted excitations substantially improved the experimental joint moment tracking accuracy than current EMG-driven models. The ability of the hybrid model to predict missing muscle EMGs was also examined. The proposed hybrid model enables muscle-driven simulations of human movement while enforcing physiological constraints on muscle excitation patterns. This might have important implications for studying pathological movement for which EMG recordings are limited.
|Barrett R.S., Besier T.F., Lloyd D.G., Individual muscle contributions to the swing phase of gait: and EMG based forward dynamics model. Simulation Modelling Practice and Theory, 15: 1146–1155, 2007. (2007) View|
Muscle activation patterns and kinematic conditions at the beginning of the swing phase of gait were used as input to a forward dynamics simulation of the swing leg. A neuromusculoskeletal model was used to account for the non-linearity between muscle excitation and muscle force outputs. Following model tuning a close agreement between simulated and measured swing phase kinematics was obtained. Simulation results suggest that swing leg muscles play an important role in controlling the motion of the swing leg during walking, and that the effect of individual muscles is not necessarily restricted to the joints they span or their basic anatomical classifications.
|Sartori M., Gizzia L., Lloyd D.G., Farina D. A musculoskeletal model of human locomotion driven by a low dimensional set of impulsive excitation primitives. Frontiers in Computational Neuroscience, 7:79. doi: 10.3389/fncom.2013.00079, 2013. (2013) View|
Human locomotion has been described as being generated by an impulsive (burst-like) excitation of groups of musculotendon units, with timing dependent on the biomechanical goal of the task. Despite this view being supported by many experimental observations on specific locomotion tasks, it is still unknown if the same impulsive controller (i.e., a low-dimensional set of time-delayed excitastion primitives) can be used as input drive for large musculoskeletal models across different human locomotion tasks. For this purpose, we extracted, with non-negative matrix factorization, five non-negative factors from a large sample of muscle electromyograms in two healthy subjects during four motor tasks. These included walking, running, sidestepping, and crossover cutting maneuvers. The extracted non-negative factors were then averaged and parameterized to obtain task-generic Gaussian-shaped impulsive excitation curves or primitives. These were used to drive a subject-specific musculoskeletal model of the human lower extremity. Results showed that the same set of five impulsive excitation primitives could be used to predict the dynamics of 34 musculotendon units and the resulting hip, knee and ankle joint moments (i.e., NRMSE = 0.18 ± 0.08, and R (2) = 0.73 ± 0.22 across all tasks and subjects) without substantial loss of accuracy with respect to using experimental electromyograms (i.e., NRMSE = 0.16 ± 0.07, and R (2) = 0.78 ± 0.18 across all tasks and subjects). Results support the hypothesis that biomechanically different motor tasks might share similar neuromuscular control strategies. This might have implications in neurorehabilitation technologies such as human-machine interfaces for the torque-driven, proportional control of powered prostheses and orthoses. In this, device control commands (i.e., predicted joint torque) could be derived without direct experimental data but relying on simple parameterized Gaussian-shaped curves, thus decreasing the input drive complexity and the number of needed sensors.
|Pizzolato C., Lloyd D.G., Sartori M., Ceseracciu E., Besier T.F., Reggiani M., CEINMS: An Opensim toolbox to investigate the influence of different neural solutions in predicting muscle excitations and joint moments during dynamic motor tasks, XXV Congress of the International Society of Biomechanics, At Glasgow, UK (2015) View|
|Winby C.R., Gerus P., Kirk T.B., Lloyd D.G., Correlation between EMG-based co-activation measures and medial and lateral compartment loads during gait. Clinical Biomechanics, 28(9-10): 1014-1019, 2013 (2013) View|
BACKGROUND: Inappropriate tibiofemoral joint contact loading during gait is thought to contribute to the development of osteoarthritis. Increased co-activation of agonist/antagonist pair of muscles during gait has commonly been observed in pathological populations and it is thought that this results in increased articular loading and subsequent risk of disease development. However, these hypotheses assume that there is a close relationship between muscle electromyography and force production, which is not necessarily the case. METHODS: This study investigated the relationship between different electromyography-based co-activation measures and articular loading during gait using an electromyography-driven model to estimate joint contact loads. FINDINGS: The results indicated that significant correlations do exist between selected electromyography-based activity measures and articular loading, but these are inconsistent and relatively low. However despite this, it was found that it may still be possible to use carefully selected measures of muscle activation in conjunction with external adduction moment measures to account for up to 50% of the variance in medial and lateral compartment loads. INTERPRETATION: The inconsistency in correlations between many electromyography-based co-activation measures and articular loading still highlights the danger of inferring joint contact loads during gait using these measures. These results suggest that some form of electromyography-driven modelling is required to estimate joint contact loads in the tibiofemoral joint.
|Winby C.R., Lloyd D.G., Kirk T.B., Evaluation of different analytical methods for subject-specific scaling of musculotendon parameters. Journal of Biomechanics, 41(8): 1682-1688, 2008. (2008) View|
Musculoskeletal models are often used to estimate internal muscle forces and the effects of those forces on the development of human movement. The Hill-type muscle model is an important component of many of these models, yet it requires specific knowledge of several muscle and tendon properties. These include the optimal muscle fibre length, the length at which the muscle can generate maximum force, and the tendon slack length, the length at which the tendon starts to generate a resistive force to stretch. Both of these parameters greatly influence the force-generating behaviour of a musculotendon unit and vary with the size of the person. However, these are difficult to measure directly and are often estimated using the results of cadaver studies, which do not account for differences in subject size. This paper examined several different techniques that can be used to scale the optimal muscle fibre length and tendon slack length of a musculotendon unit according to subject size. The techniques were divided into three categories corresponding to linear scaling, scaling by maintaining a constant tendon slack length throughout the range of joint motion, and scaling by maintaining muscle operating range throughout the range of joint motion. We suggest that a good rationale for scaling muscle properties should be to maintain the same force-generating characteristics of a musculotendon unit for all subjects, which is best achieved by scaling that preserves the muscle operating range when the muscle is maximally activated.
|Winby C.R., Lloyd D.G., Besier T.F. Kirk T.B., Muscle and external load contribution to knee joint contact loads during normal gait. Journal of Biomechanics, 42(14): 2294–2300, 2009. (2009) View|
Large knee adduction moments during gait have been implicated as a mechanical factor related to the progression and severity of tibiofemoral osteoarthritis and it has been proposed that these moments increase the load on the medial compartment of the knee joint. However, this mechanism cannot be validated without taking into account the internal forces and moments generated by the muscles and ligaments, which cannot be easily measured. Previous musculoskeletal models suggest that the medial compartment of the tibiofemoral joint bears the majority of the tibiofemoral load, with the lateral compartment unloaded at times during stance. Yet these models did not utilise explicitly measured muscle activation patterns and measurements from an instrumented prosthesis which do not portray lateral compartment unloading. This paper utilised an EMG-driven model to estimate muscle forces and knee joint contact forces during healthy gait. Results indicate that while the medial compartment does bear the majority of the load during stance, muscles provide sufficient stability to counter the tendency of the external adduction moment to unload the lateral compartment. This stability was predominantly provided by the quadriceps, hamstrings, and gastrocnemii muscles, although the contribution from the tensor fascia latae was also significant. Lateral compartment unloading was not predicted by the EMG-driven model, suggesting that muscle activity patterns provide useful input to estimate muscle and joint contact forces.
|Sartori M., Reggiani, M., van den Bogert A.J., Lloyd D.G. Estimation of musculotendon kinematics in large musculoskeletal models using multidimensional B-Splines. Journal of Biomechanics, 45(3), 595-601, 2012. (2012) View|
We present a robust and computationally inexpensive method to estimate the lengths and three-dimensional moment arms for a large number of musculotendon actuators of the human lower limb. Using a musculoskeletal model of the lower extremity, a set of values was established for the length of each musculotendon actuator for different lower limb generalized coordinates (joint angles). A multidimensional spline function was then used to fit these data. Muscle moment arms were obtained by differentiating the musculotendon length spline function with respect to the generalized coordinate of interest. This new method was then compared to a previously used polynomial regression method. Compared to the polynomial regression method, the multidimensional spline method produced lower errors for estimating musculotendon lengths and moment arms throughout the whole generalized coordinate workspace. The fitting accuracy was also less affected by the number of dependent degrees of freedom and by the amount of experimental data available. The spline method only required information on musculotendon lengths to estimate both musculotendon lengths and moment arms, thus relaxing data input requirements, whereas the polynomial regression requires different equations to be used for both musculotendon lengths and moment arms. Finally, we used the spline method in conjunction with an electromyography driven musculoskeletal model to estimate muscle forces under different contractile conditions, which showed that the method is suitable for the integration into large scale neuromusculoskeletal models.
|Sartori M, Reggiani M, Farina D, Lloyd DG (2012) EMG-Driven Forward-Dynamic Estimation of Muscle Force and Joint Moment about Multiple Degrees of Freedom in the Human Lower Extremity. PLoS ONE 7(12): e52618. doi:10.1371/journal.pone.0052618 (2012) View|
This work examined if currently available electromyography (EMG) driven models, that are calibrated to satisfy joint moments about one single degree of freedom (DOF), could provide the same musculotendon unit (MTU) force solution, when driven by the same input data, but calibrated about a different DOF. We then developed a novel and comprehensive EMG-driven model of the human lower extremity that used EMG signals from 16 muscle groups to drive 34 MTUs and satisfy the resulting joint moments simultaneously produced about four DOFs during different motor tasks. This also led to the development of a calibration procedure that allowed identifying a set of subject-specific parameters that ensured physiological behavior for the 34 MTUs. Results showed that currently available single-DOF models did not provide the same unique MTU force solution for the same input data. On the other hand, the MTU force solution predicted by our proposed multi-DOF model satisfied joint moments about multiple DOFs without loss of accuracy compared to single-DOF models corresponding to each of the four DOFs. The predicted MTU force solution was (1) a function of experimentally measured EMGs, (2) the result of physiological MTU excitation, (3) reflected different MTU contraction strategies associated to different motor tasks, (4) coordinated a greater number of MTUs with respect to currently available single-DOF models, and (5) was not specific to an individual DOF dynamics. Therefore, our proposed methodology has the potential of producing a more dynamically consistent and generalizable MTU force solution than was possible using single-DOF EMG-driven models. This will help better address the important scientific questions previously approached using single-DOF EMG-driven modeling. Furthermore, it might have applications in the development of human-machine interfaces for assistive devices.
|Buchanan T.S, Lloyd D.G., Manal K., Besier, T.F., Neuromusculoskeletal modelling: estimation of muscle forces and joint moments and movements from measurements of neural command. Journal of Applied Biomechanics, 20(4): 367-395, 2004, (2004) View|
This paper provides an overview of forward dynamic neuromusculoskeletal modeling. The aim of such models is to estimate or predict muscle forces, joint moments, and/or joint kinematics from neural signals. This is a four-step process. In the first step, muscle activation dynamics govern the transformation from the neural signal to a measure of muscle activation-a time varying parameter between 0 and 1. In the second step, muscle contraction dynamics characterize how muscle activations are transformed into muscle forces. The third step requires a model of the musculoskeletal geometry to transform muscle forces to joint moments. Finally, the equations of motion allow joint moments to be transformed into joint movements. Each step involves complex nonlinear relationships. The focus of this paper is on the details involved in the first two steps, since these are the most challenging to the biomechanician. The global process is then explained through applications to the study of predicting isometric elbow moments and dynamic knee kinetics.