Calculate Muscle Forces Research Articles

Assessment of the energy-related cost function over a range of walking speeds.

Cost funtions are needed for calculation of muscle forces in musculoskeletal models. The behavior of the energy-related cost function, proposed by Praagman et al. (J Biomech 39(4):758-765, 2006. https://doi.org/10.1016/j.jbiomech.2004.11.034 ) (CFP), can be used as an optimization criteria in musculoskeletal models for studying gait. In particular, in this work, its performance is compared against two empirical phenomenological models at different walking speed conditions. Also, the sensitivity of the CFP function to model parameters, such as muscle mass, maximal isometric muscle force, optimal muscle fiber length and maximum muscle velocity of the contractile element, was analyzed. The obtained results showed that CFP presents different behavior (in terms of the normalized root-mean-squared deviation (NRMSD) and the coefficient of multiple correlation (CMC)) for different muscles. Also, it provided estimates with median of NRMSD between 0.176 and 0.299 and median of CMC between 0.703 and 0.865 both metrics for slow, free and fast walking speed, which could be considered as acceptable results. Furthermore, the results indicated that CFP is insensitive to changes in muscle mass and relatively sensitive to maximal isometric muscle force. However, CFP presented a noisy behavior on estimations of muscle energy rate for some muscle as compared to phenomenological models. Finally, estimations by CFP during gait are within the values obtained by the empirical phenomenological models.

Selective dorsal rhizotomy improves muscle forces during walking in children with spastic cerebral palsy

BackgroundSelective dorsal rhizotomy aims to reduce spasticity in children with cerebral palsy. Early investigations indicated postoperative weakness, whereas more recent studies showed that selective dorsal rhizotomy either does not change or improves muscle strength. All previous studies assessed muscle strength in a static position, which did not represent the walking situation. The aim of this study was to analyze the influence of selective dorsal rhizotomy on muscle forces during gait. MethodsMotion capture data of 25 children with spastic cerebral palsy and 10 typically developing participants were collected. A musculoskeletal OpenSim model was used to calculate joint kinematics, joint kinetics and muscle forces during gait. Static optimization and an electromyography-informed approach to calculate muscle forces were compared. A Muscle-Force-Profile was introduced and used to compare the muscle forces during walking before and after a selective dorsal rhizotomy. FindingsIndependent of the approach used (electromyography-informed versus static optimization), selective dorsal rhizotomy significantly normalized forces in spastic muscles during walking and did not reduce the contribution of non-spastic muscles. InterpretationThis study showed that selective dorsal rhizotomy improves dynamic muscle forces in children with cerebral palsy and leads to less gait pathology, as shown in the improvement in joint kinematics and joint kinetics. Individual muscle force analyses using the Muscle-Force-Profile extend standard joint kinematics and joint moment analyses, which might improve clinical-decision making in children with cerebral palsy in the future. The reference data of our participants and MATLAB code for the Muscle-Force-Profile are publicly available on simtk.org/projects/muscleprofile.

Subject-specific loads on the lumbar spine in detailed finite element models scaled geometrically and kinematic-driven by radiography images.

Traditional load-control musculoskeletal and finite element (FE) models of the spine fail to accurately predict in vivo intervertebral joint loads due mainly to the simplifications and assumptions when estimating redundant trunk muscle forces. An alternative powerful protocol that bypasses the calculation of muscle forces is to drive the detailed FE models by image-based in vivo displacements. Development of subject-specific models, however, both involves the risk of extensive radiation exposures while imaging in supine and upright postures and is time consuming in terms of the reconstruction of the vertebrae, discs, ligaments, and facets geometries. This study therefore aimed to introduce a remedy for the development of subject-specific FE models by scaling the geometry of an existing detailed FE model of the T12-S1 lumbar spine. Five subject-specific scaled models were driven by their own radiography image-based displacements in order to predict joint loads, ligament forces, facet joint forces, and disc fiber strains during relaxed upright as well as moderate flexion and extension tasks. The predicted intradiscal pressures were found in adequate agreement with in vivo data for upright, flexion, and extension tasks. There were however large intersubject variations in the estimated joint loads and facet forces.

Calculation of Muscle Activity during Race Walking

Race walking has been shown to be a lower impact alternative to running. In fact, lower injury rates are reported in race walkers than runners. This may be due to lower joint forces. However, this theory has not been explicitly tested since joint contact forces are difficult to measure. Therefore, these joint forces are calculated using either a serial or cosimulation approach. In both strategies, a detailed joint model and multibody dynamics model are used in tandem to calculate muscle forces. These muscle forces then contribute to the joint contact forces. Computed muscle control (CMC) is an algorithm that utilizes a proportional-derivative controller, static optimization, and forward dynamics to calculate muscle activations, which are used to subsequently calculate muscle forces. The goal of this study was to investigate the viability of using CMC to calculate muscle activations during race walking, with the intent of using these activations in future studies to calculate joint forces. Instrumented motion capture data (kinematics, kinetics, and electromyography (EMG)) of a representative subject was used from a previous study. In this study, subjects walked, race walked, and ran at a self-selected pace across a walkway with embedded forceplates while marker trajectories and EMG were collected. Muscle actuated forward dynamics simulations (i.e. CMC) were created for each movement. The muscle activations resulting from these CMC simulations were compared to the experimentally measured EMG by performing a cross-correlation. The CMC results were fairly accurate across all muscles (gluteus medius, rectus femoris, vastus lateralis, adductor longus, semitendinosus, tibialis anterior, gastrocnemius, peroneus longus) with correlation coefficients greater than 0.5. There was no apparent relationship between movement type and coefficient. Future work is needed to determine if correlation coefficients of 0.5 are accurate enough for studies looking to accurately quantify joint contact forces.

Hypersensitivity of trunk biomechanical model predictions to errors in image-based kinematics when using fully displacement-control techniques

Recent advances in medical imaging techniques have allowed pure displacement-control trunk models to estimate spinal loads with no need to calculate muscle forces. Sensitivity of these models to the errors in post-imaging evaluation of displacements (reported to be ∼0.4–0.9° and 0.2–0.3 mm in vertebral displacements) has not yet been investigated. A Monte Carlo analysis was therefore used to assess the sensitivity of results in both musculoskeletal (MS) and passive finite element (FE) spine models to errors in measured displacements. Six static activities in upright standing, flexed, and extended postures were initially simulated using a force-control hybrid MS-FE model. Computed vertebral displacements were subsequently used to drive two distinct fully displacement-control MS and FE models. Effects of alterations in the reference vertebral displacements (at 3 error levels with SD (standard deviation) = 0.1, 0.2, and 0.3 mm in input translations together with, respectively, 0.2, 0.4, and 0.6° in input rotations) were investigated on the model predictions. Results indicated that outputs of both models had substantial task-dependent sensitivities to errors in the measured vertebral translations. For instance, L5-S1 intradiscal pressures (IDPs) were considerably affected (SD values reaching 1.05 MPa) and axial compression and shear forces even reversed directions as translation errors increased to 0.3 mm. Outputs were however generally much less sensitive to errors in measured vertebral rotations. Accounting for the accuracies in image-based kinematics measurements, therefore, it is concluded that the current measured vertebral translation errors at and beyond 0.1 mm are too large to drive biomechanical models of the spine.

The influence of rotator cuff tears on muscle and joint-contact loading after reverse total shoulder arthroplasty.

Rotator cuff tears are known to affect clinical outcome of reverse total shoulder arthroplasty (RSA). This study aimed to use computational modelling to quantify the effect of rotator cuff tear severity on muscle and joint forces after RSA, as well as stresses at the glenosphere, base-plate, fixation screws, scapula, and humeral components. A multi-body musculoskeletal model of the glenohumeral joint was developed comprising the scapula, humerus and nine major upper limb muscles. Simulations of abduction and flexion after RSA were performed in the case of the intact rotator cuff and tears to (i) supraspinatus; (ii) supraspinatus and infraspinatus, and (iii) supraspinatus, infraspinatus and subscapularis. The intact and supraspinatus deficient rotator cuff resulted in the largest calculated muscle forces, glenohumeral joint contact forces and implant stresses. Peak glenohumeral joint forces during flexion were lower than those during abduction in all cases; however, substantially more posterior joint shear force was generated during flexion than abduction. A tear involving the supraspinatus and infraspinatus reduced glenohumeral joint forces by a factor of 8.7 during abduction (603.1 N) and 7.1 during flexion (520.7 N) compared to those in the supraspinatus deficient shoulder. RSA with an intact or supraspinatus deficient rotator cuff produces large glenohumeral joint forces that may increase base-plate failure risk, particularly during flexion when posterior shear forces are largest. Infraspinatus tears after RSA greatly reduce glenohumeral joint compression and may ultimately reduce joint stability. Future research ought to focus on experimental validation of subject-specific muscle recruitment strategies and joint loading after RSA. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res.

O 107 – Impact of subject-specific musculoskeletal geometry on estimated joint kinematics, joint kinetics and muscle forces in typically developing children

1.. Introduction: Gait analysis together with musculoskeletal modeling can be used to calculate muscle forces and assess pathological gait [1]. No generic, pediatric musculoskeletal models are available and, therefore, linear scaling methods are commonly used to personalize a generic, adult musculoskeletal model to the child’s anthropometry. 2. Research: How different are joint kinematics, joint kinetics and muscle force estimates of generic scaled models compared to medical-imaging based models in typically developing (TD) children? 3. Methods: 3D motion capture data and magnetic resonance images (MRI) of a TD boy (age: 8 years; height: 1.23 m; weight: 20.4 kg) were collected. Two musculoskeletal OpenSim models were created: (1) a scaled generic model (M_gen), and (2) a MRI-based model, which included subject-specific musculoskeletal geometry (M_mri) [2]. Joint kinematics, joint kinetics and muscle forces were calculated for each model using OpenSim 3.3 [3]. Joint kinematics, joint kinetics, muscle force waveforms, as well as femoral anteversion angle, neck-shaft angle and hip joint centre location were compared between both models. 4. Results: Joint kinematics and joint kinetics were surprisingly similar between the M_gen and M_mri with root-mean-square-differences below 2.8° and 0.05Nm/kg for joint angles and moments, respectively (Fig. 1, Fig. 2). Depending on the analyzed muscle, differences in muscle forces varied substantially (up to 230% difference) between the M_gen and M_mri (Fig. 3). Femoral anteversion and neck-shaft angles differed between M_gen and M_mri by 12 and 5 degrees, respectively. The hip joint centre position differed between both models by 5, 15 and 6 mm in the anterior/posterior, superior/inferior and medial/lateral direction, respectively.

A novel stability-based EMG-assisted optimization method for the spine

Traditional electromyography-assisted optimization (TEMG) models are commonly employed to compute trunk muscle forces and spinal loads for the design of clinical/treatment and ergonomics/prevention programs. These models calculate muscle forces solely based on moment equilibrium requirements at spinal joints. Due to simplifications/assumptions in the measurement/processing of surface EMG activities and in the presumed muscle EMG-force relationship, these models fail to satisfy stability requirements. Hence, the present study aimed to develop a novel stability-based EMG-assisted optimization (SEMG) method applied to a musculoskeletal spine model in which trunk muscle forces were estimated by enforcing equilibrium conditions constrained to stability requirements. That is, second-order partial derivatives of the potential energy of the musculoskeletal model with respect to its generalized coordinates were enforced to be positive semi-definite. Fifteen static tasks in upright and flexed postures with and without a hand load at different heights were simulated. The SEMG model predicted different muscle recruitments/forces (generally larger global and local muscle forces) and spinal loads (slightly larger) compared to the TEMG model. Such task-specific differences were dependant on the assumed magnitude of the muscle stiffness coefficient in the SEMG model. The SEMG model-predicted and measured L4-L5 intradiscal pressures were in satisfactory agreement during simulated activities.

Joint moments and contact forces in the foot during walking

The net force and moment of a joint have been widely used to understand joint disease in the foot. Meanwhile, it does not reflect the physiological forces on muscles and contact surfaces. The objective of the study is to estimate active moments by muscles, passive moments by connective tissues and joint contact forces in the foot joints during walking. Joint kinematics and external forces of ten healthy subjects (all males, 24.7 ± 1.2 years) were acquired during walking. The data were entered into the five-segment musculoskeletal foot model to calculate muscle forces and joint contact forces of the foot joints using an inverse dynamics-based optimization. Joint reaction forces and active, passive and net moments of each joint were calculated from muscle and ligament forces. The maximum joint reaction forces were 8.72, 4.31, 2.65, and 3.41 body weight (BW) for the ankle, Chopart’s, Lisfranc and metatarsophalangeal joints, respectively. Active and passive moments along with net moments were also obtained. The maximum net moments were 8.6, 8.4, 5.4 and 0.8%BW∙HT, respectively. While the trend of net moment was very similar between the four joints, the magnitudes and directions of the active and passive moments varied between joints. The active and passive moments during walking could reveal the roles of muscles and ligaments in each of the foot joints, which was not obvious in the net moment. This method may help narrow down the source of joint problems if applied to clinical studies.

Predictive Simulations of Neuromuscular Coordination and Joint-Contact Loading in Human Gait.

We implemented direct collocation on a full-body neuromusculoskeletal model to calculate muscle forces, ground reaction forces and knee contact loading simultaneously for one cycle of human gait. A data-tracking collocation problem was solved for walking at the normal speed to establish the practicality of incorporating a 3D model of articular contact and a model of foot-ground interaction explicitly in a dynamic optimization simulation. The data-tracking solution then was used as an initial guess to solve predictive collocation problems, where novel patterns of movement were generated for walking at slow and fast speeds, independent of experimental data. The data-tracking solutions accurately reproduced joint motion, ground forces and knee contact loads measured for two total knee arthroplasty patients walking at their preferred speeds. RMS errors in joint kinematics were < 2.0° for rotations and < 0.3cm for translations while errors in the model-computed ground-reaction and knee-contact forces were < 0.07 BW and < 0.4 BW, respectively. The predictive solutions were also consistent with joint kinematics, ground forces, knee contact loads and muscle activation patterns measured for slow and fast walking. The results demonstrate the feasibility of performing computationally-efficient, predictive, dynamic optimization simulations of movement using full-body, muscle-actuated models with realistic representations of joint function.

Development of a human head and neck muscle activation control model based on BPNN

With the development of vehicle active safety technology and its combination with passive safety technologies, there is a need for an updated tool to assess the performance of vehicle safety systems in both the in-crash and pre-crash phases. Consequently, developing human body models with active muscles is critical to achieve improved bio-fidelity simulation results to evaluate the effects of safety systems. In the present study, an innovative human active muscle control model based on a back-propagation neural network (BPNN) controller was developed, and the active muscle response was modelled using a feedback control strategy with the BPNN algorithm implemented in a human head and neck Sim Mechanics model. The BPNN controller was used to dynamically control the muscle activation level, the most complicated and essential factor in active muscle force calculation based on the Hill equation. Then, the active muscle control model was evaluated by simulating the human response in volunteer sled tests. The results validated the proposed active muscle control method as successful and easily realized. Achieving muscle active control would further improve the bio-fidelity of the human body model.

The Effects of Filter Cutoff Frequency on Musculoskeletal Simulations of High-Impact Movements.

Estimation of muscle forces through musculoskeletal simulation is important in understanding human movement and injury. Unmatched filter frequencies used to low-pass filter marker and force platform data can create artifacts during inverse dynamics analysis, but their effects on muscle force calculations are unknown. The objective of this study was to determine the effects of filter cutoff frequency on simulation parameters and magnitudes of lower-extremity muscle and resultant joint contact forces during a high-impact maneuver. Eight participants performed a single-leg jump landing. Kinematics was captured with a 3D motion capture system, and ground reaction forces were recorded with a force platform. The marker and force platform data were filtered using 2 matched filter frequencies (10-10Hz and 15-15Hz) and 2 unmatched filter frequencies (10-50Hz and 15-50Hz). Musculoskeletal simulations using computed muscle control were performed in OpenSim. The results revealed significantly higher peak quadriceps (13%), hamstrings (48%), and gastrocnemius forces (69%) in the unmatched (10-50Hz and 15-50Hz) conditions than in the matched (10-10Hz and 15-15Hz) conditions (P < .05). Resultant joint contact forces and reserve (nonphysiologic) moments were similarly larger in the unmatched filter categories (P < .05). This study demonstrated that artifacts created from filtering with unmatched filter cutoffs result in altered muscle forces and dynamics that are not physiologic.

Calculation of muscle forces and joint reaction loads in the shoulder area via an OpenSim based computer model

Abstract Using the OpenSim software and verified anatomical data, a computer model for the calculation of biomechanical parameters is developed and used to determine the effect of a reattachment of the Supraspinatus muscle with a medial displacement of the muscle attachment point, which may be necessary for a rupture of the supraspinatus tendon. The results include the influence of the operation on basic biomechanical parameters such as the lever arm, as well as the calculated the muscle activations for the supraspinatus and deltoid. In addition, the influence on joint stability is examined by an analysis of the joint reaction force. The study provides a detailed description of the used model, as well as medical findings to a reattachment of the supraspinatus.

Effect of Constraint Loading on the Lower Limb Muscle Forces in Weightless Treadmill Exercise.

Long exposure to the microgravity will lead to muscle atrophy and bone loss. Treadmill exercise could mitigate the musculoskeletal decline. But muscle atrophy remains inevitable. The constraint loading applied on astronauts could affect the muscle force and its atrophy severity. However, the quantitative correlation between constraint loading mode and muscle forces remains unclear. This study aimed to characterize the influence of constraint loading mode on the lower limb muscle forces in weightless treadmill exercise. The muscle forces in the full gait cycle were calculated with the inverse dynamic model of human musculoskeletal system. The calculated muscle forces at gravity were validated with the EMG data. Muscle forces increased at weightlessness compared with those at the earth's gravity. The increasing percentage from high to low is as follows: biceps femoris, gastrocnemius, soleus, vastus, and rectus femoris, which was in agreement with the muscle atrophy observed in astronauts. The constraint loading mode had an impact on the muscle forces in treadmill exercise and thus could be manipulated to enhance the effect of the muscle training in spaceflight. The findings could provide biomechanical basis for the optimization of treadmill constraint system and training program and improve the countermeasure efficiency in spaceflight.

The effect of leg dominance and landing height on ACL loading among female athletes

Female athletes are more prone to anterior cruciate ligament (ACL) injury. A neuromuscular imbalance called leg dominance may provide a biomechanical explanation. Therefore, the purpose of this study was to compare the side-to-side lower limb differences in movement patterns, muscle forces and ACL forces during a single-leg drop-landing task from two different heights. We hypothesized that there will be significant differences in lower limb movement patterns (kinematics), muscle forces and ACL loading between the dominant and non-dominant limbs. Further, we hypothesized that significant differences between limbs will be present when participants land from a greater drop-landing height. Eight recreational female participants performed dominant and non-dominant single-leg drop landings from 30 to 60cm. OpenSim software was used to develop participant-specific musculoskeletal models and to calculate muscle forces. We also predicted ACL loading using our previously established method. There were no significant differences between dominant and non-dominant leg landing except in ankle dorsiflexion and GMED muscle forces at peak GRF. Landing from a greater height resulted in significant differences among most kinetics and kinematics variables and ACL forces. Minimal differences in lower-limb muscle forces and ACL loading between the dominant and non-dominant legs during single-leg landing may suggest similar risk of injury across limbs in this cohort. Further research is required to confirm whether limb dominance may play an important role in the higher incidence of ACL injury in female athletes with larger and sport-specific cohorts.

Effects of Anterior Knee Displacement During Squatting on Patellofemoral Joint Stress.

Squatting is a common rehabilitation training exercise for patellofemoral pain syndrome (PFPS). Patellofemoral joint stress (PFJS) during squatting with more anterior knee displacement has not been systematically investigated. To compare PFJS during squatting using 2 techniques: squat while keeping the knees behind the toes (SBT) and squat while allowing the knees to go past the toes (SPT). University research laboratory. Twenty-five healthy females (age: 22.69 (0.74) y; height: 169.39 (6.44) cm; mass: 61.55 (9.74) kg) participated. Three-dimensional kinematic and kinetic data were collected at 180 and 1800Hz, respectively. A musculoskeletal model was used to calculate muscle forces through static optimization. These muscle forces were used in a patellofemoral joint model to estimate PFJS. The magnitudes of PFJS, reaction force, and quadriceps force were higher (P < .001) during SPT compared with the SBT technique. Knee flexion, hip flexion, and ankle dorsiflexion angles were reduced when using the SBT technique. Findings provide some general support for minimizing forward knee translation during squats for patients that may have patellofemoral pain syndrome.

Pelvic and lower extremity physiological cross-sectional areas: an MRI study of the living young and comparison to published research literature.

Morphological data pertaining to the pelvis and lower extremity muscles are increasingly being used in biomechanical modeling to compare healthy and pathological conditions. Very few data sets exist that encompass all of the muscles of the lower limb, allowing for comparisons between regions. The aims of this study were to (a) provide physiological cross-sectional area (PCSA) data for the pelvic, thigh, and leg muscles in young, healthy participants, using magnetic resonance imaging (MRI), and (b) to compare these data with summarized PCSAs obtained from the literature. Six young and healthy volunteers participated and were scanned using 3T MRI. PCSAs were calculated from volumetric segmentations obtained bilaterally of 28 muscles/muscle groups of the pelvis, thigh, and leg. These data were compared to published, summarized PCSA data derived from cadaveric, computed tomography, MRI and ultrasound studies. The PCSA of the pelvis, thigh, and leg muscles tended to be 20-130% larger in males than in females, except for the gemelli which were 34% smaller in males, and semitendinosus and triceps surae which did not differ (<20% different). The dominant and the non-dominant sides showed similar and minutely different PCSA with less than 18% difference between sides. Comparison to other studies revealed wide ranges within, and large differences between, the cadaveric and imaging PCSA data. Comparison of the PCSA of this study and published literature revealed major differences in the iliopsoas, gluteus minimus, tensor fasciae latae, gemelli, obturator internus, biceps femoris, quadriceps femoris, and the deep leg flexor muscles. These volume-derived PCSAs of the pelvic and lower limb muscles alongside the data synthesised from the literature may serve as a basis for comparative and biomechanical studies of the living and healthy young, and enable calculation of muscle forces. Comparison of the literature revealed large variations in PCSA from each of the different investigative modalities, hampering comparability between studies. Sample size, age, post-mortem changes of muscle tone, chemical fixation of cadaveric tissues, and the underlying physics of the imaging techniques may potentially influence PCSA calculations.

The sensitivity of shoulder muscle and joint force predictions to changes in joint kinematics: A Monte-Carlo analysis

Kinematics of the shoulder girdle obtained from non-invasive measurement systems such as video motion analysis, accelerometers and magnetic tracking sensors has been shown to be adversely affected by instrumentation measurement errors and skin motion artefact. The degree to which musculoskeletal model calculations of shoulder muscle and joint loading are influenced by variations in joint kinematics is currently not well understood. A three-dimensional musculoskeletal model of the upper limb was used to evaluate the sensitivity of shoulder muscle and joint force. Monte-Carlo analyses were performed by randomly perturbing scapular and humeral joint coordinates during abduction and flexion. Muscle and joint force calculations were generally most sensitive to changes in the kinematics of the humerus in elevation and of the scapula in medial-lateral rotation, and were least sensitive to changes in humerus plane of elevation and scapula protraction-retraction. Overall model sensitivity was greater during abduction than flexion, and the influence of specific kinematics perturbations varied from muscle to muscle. In general, muscles that generated greater force, such as the middle deltoid and subscapularis, were more sensitive to changes in shoulder kinematics. This study suggests that musculoskeletal model sensitivity to changes in kinematics is task-specific, and varies depending on the plane of motion. Calculations of shoulder muscle and joint function depend on reliable humeral and scapula motion data, particularly that of humeral elevation and scapula medial-lateral rotation. The findings in this study have implications for the use of kinematic data in musculoskeletal model development and simulations.

Calculation of muscle forces during normal gait under consideration of femoral bending moments

This paper introduces a new approach for computing lower extremity muscle forces by incorporating equations that consider “bone structure” and “prevention of bending by load reduction” into existing optimization algorithms.Lower extremity muscle and joint forces, during normal gait, were calculated and compared using two different optimization approaches. We added constraint equations that prevent femoral bending loads to an existing approach that considers “minimal total muscular force”. Gait parameters such as kinematics, ground reaction forces, and surface electromyographic activation patterns were examined using standardized gait analysis. A subject-specific anatomic model of the lower extremities, obtained from magnetic resonance images of a healthy male, was used for the simulations. Finite element analysis was used to calculate femoral loads.The conventional method of calculating muscle forces leads to higher rates of femoral bending and structural stress than the new approach. Adding equations with structural subject-specific parameters in our new approach resulted in reduced femoral stress patterns.These findings show that our new approach improves the accuracy of femoral stress and strain simulations. Structural overloads caused by bending can be avoided during inverse calculation of muscle forces.

CEINMS: A toolbox to investigate the influence of different neural control solutions on the prediction of muscle excitation and joint moments during dynamic motor tasks

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.