Estimate Muscle Forces Research Articles

Muscle force estimation in clinical gait analysis using AnyBody and OpenSim

A variety of musculoskeletal models are applied in different modelling environments for estimating muscle forces during gait. Influence of different modelling assumptions and approaches on model outputs are still not fully understood, while direct comparisons of standard approaches have been rarely undertaken. This study seeks to compare joint kinematics, joint kinetics and estimated muscle forces of two standard approaches offered in two different modelling environments (AnyBody, OpenSim). It is hypothesised that distinctive differences exist for individual muscles, while summing up synergists show general agreement. Experimental data of 10 healthy participants (28 ± 5 years, 1.72 ± 0.08 m, 69 ± 12 kg) was used for a standard static optimisation muscle force estimation routine in AnyBody and OpenSim while using two gait-specific musculoskeletal models. Statistical parameter mapping paired t-test was used to compare joint angle, moment and muscle force waveforms in Matlab. Results showed differences especially in sagittal ankle and hip angles as well as sagittal knee moments. Differences were also found for some of the muscles, especially of the triceps surae group and the biceps femoris short head, which occur as a result of different anthropometric and anatomical definitions (mass and inertia of segments, muscle properties) and scaling procedures (static vs. dynamic). Understanding these differences and their cause is crucial to operate such modelling environments in a clinical setting. Future research should focus on alternatives to classical generic musculoskeletal models (e.g. implementation of functional calibration tasks), while using experimental data reflecting normal and pathological gait to gain a better understanding of variations and divergent behaviour between approaches.

Musculoskeletal models with generic and subject-specific geometry estimate different joint biomechanics in dysplastic hips

Optimizing the geometric complexity of musculoskeletal models is important for reliable yet feasible estimation of joint biomechanics. This study investigated the effects of subject-specific model geometry on hip joint reaction forces (JRFs) and muscle forces in patients with developmental dysplasia of the hip (DDH) and healthy controls. For nine DDH and nine control subjects, three models were created with increasingly subject-specific pelvis geometry, hip joint center locations and muscle attachments. Hip JRFs and muscle forces during a gait cycle were compared among the models. For DDH subjects, resultant JRFs from highly specific models including subject-specific pelvis geometry, joint locations and muscle attachments were not significantly different compared to models using generic geometry in early stance, but were significantly higher in late stance (p = 0.03). Estimates from moderately specific models using CT-informed scaling of generic pelvis geometry were not significantly different from low specificity models using generic geometry scaled with skin markers. For controls, resultant JRFs in early stance from highly specific models were significantly lower than moderate and low specificity models (p ≤ 0.02) with no significant differences in late stance. Inter-model JRF differences were larger for DDH subjects than controls. Inter-model differences for JRF components and muscle forces were similar to resultant JRFs. Incorporating subject-specific pelvis geometry significantly affects JRF and muscle force estimates in both DDH and control groups, which may be especially important for reliable estimation of pathomechanics in dysplastic hips.

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.

MusIC method enhancement by a sensitivity study of its performance: Application to a lower limbs musculoskeletal model

Providing a biomechanical feedback during experimental sessions is a real outcome for rehabilitation, ergonomics or training applications. However, such applications imply a fast computation of the biomechanical quantities to be observed. The MusIC method has been designed to solve quickly the muscle forces estimation problem, thanks to a database interpolation. The current paper aims at enhancing its performance. Without generating any database, the method allows to identify optimal densities (number of samples contained in the database) with respect to the method accuracy and the off-line computation time needed to generate the database. On a lower limbs model (12 degrees of freedom, 82 muscles), thanks to this work, the MusIC method exhibits an accuracy error of 0.1% with an off-line computation time lower than 10 minutes. The on-line computation frequency (number of samples computed per second) is about 58 Hz. Thanks to these improvements, the MusIC method can be used to produce a feedback during an experimentation with a wide variety of musculoskeletal models or cost functions (used to share forces into muscles). The interaction between the subject, the experimenter (e.g. trainer, ergonomist or clinician) and the biomechanical data (e.g. muscle forces) in experimental sessions is a promising way to enhance rehabilitation, training or design techniques.

From deep learning to transfer learning for the prediction of skeletal muscle forces.

Skeletal muscle forces may be estimated using rigid musculoskeletal models and neural networks. Neural network (NN) approach has the advantages of real-time estimation ability and promising prediction accuracy. However, most of the developed NN models are based on conventional feedforward NNs, which do not take dynamic temporal relationships of the muscle force profiles into consideration. The objectives of this present paper are twofold: (1) to develop a recurrent deep neural network (RDNN) incorporating dynamic temporal relationships to estimate skeletal muscle forces from kinematics data during a gait cycle; (2) then to establish a transfer learning strategy to improve the accuracy of muscle force estimation. A long short-term memory (LSTM) model as a RDNN was developed and evaluated. A weight transfer strategy was established. Three databases were established for training and evaluation purposes. The predictions of rectus femoris, soleus, and tibialis anterior forces with developed LSTM network show root mean square error range of 2.4-84.6N. Relative root mean square error (RMSE) deviations for internal and external validations are less than 5% and 10% for all analyzed muscles respectively. Pearson correlation coefficients (R) range of 0.95-0.999 showed perfect waveform similarity between data and predicted muscle forces for all analyzed muscles. The use of weight transfer leads to an improvement of 1.3% for the relative deviation between simulation outcome and LSMT prediction. This present study suggests that the recurrent deep neural network is a powerful and accurate computational tool for the prediction of skeletal muscle forces. Moreover, the coupling between this deep learning approach and a transfer learning strategy leads to improve the prediction accuracy. In future work, this coupling approach will be incorporated into a developed decision support tool for functional rehabilitation with real-time estimation and tracking of skeletal muscle forces. Graphical abstract ᅟ.

Loading of the hip and knee joints during whole body vibration training.

During whole body vibrations, the total contact force in knee and hip joints consists of a static component plus the vibration-induced dynamic component. In two different cohorts, these forces were measured with instrumented joint implants at different vibration frequencies and amplitudes. For three standing positions on two platforms, the dynamic forces were compared to the static forces, and the total forces were related to the peak forces during walking. A biomechanical model served for estimating muscle force increases from contact force increases. The median static forces were 122% to 168% (knee), resp. 93% to 141% (hip), of the body weight. The same accelerations produced higher dynamic forces for alternating than for parallel foot movements. The dynamic forces individually differed much between 5.3% to 27.5% of the static forces in the same positions. On the Powerplate, they were even close to zero in some subjects. The total forces were always below 79% of the forces during walking. The dynamic forces did not rise proportionally to platform accelerations. During stance (Galileo, 25 Hz, 2 mm), the damping of dynamic forces was only 8% between foot and knee but 54% between knee and hip. The estimated rises in muscle forces due to the vibrations were in the same ranges as the contact force increases. These rises were much smaller than the vibration-induced EMG increases, reported for the same platform accelerations. These small muscle force increases, along with the observation that the peak contact and muscle forces during vibrations remained far below those during walking, indicate that dynamic muscle force amplitudes cannot be the reason for positive effects of whole body vibrations on muscles, bone remodelling or arthritic joints. Positive effects of vibrations must be caused by factors other than raised forces amplitudes.

Sex differences in gluteal muscle forces during running

ABSTRACT Sex differences in common sports injuries to the lower extremity have been reported. Biomechanical factors of the hip have been investigated between sexes with regard to running-related injury. This study investigates gluteal muscle forces between sexes to aid in our understanding of sex-related biomechanical factors in running. Twenty-one healthy male and female runners were participants. Each ran at a controlled speed of 3.52–3.89 m/s down a 20-m runway. Kinetic and kinematic data were utilised to estimate muscle forces. Multivariate analysis of variance tests were utilised to detect differences in gluteal and hamstring muscle forces, hip and pelvic kinematics, and hip kinetic variables between sexes. Males produced greater peak gluteus maximus force, but lesser peak gluteus medius, minimus, and hamstring force than females during running. Males also demonstrated less hip adduction and greater hip flexion and anterior pelvic tilt than females. Finally, males produced lesser peak hip abduction and external rotation moments than females. Males and females demonstrate differences in gluteal muscle forces and hip kinetics and kinematics during running. Further understanding of underlying muscle forces may offer further insight into biomechanical differences in running between males and females.

A calibrated EMG-informed neuromusculoskeletal model can appropriately account for muscle co-contraction in the estimation of hip joint contact forces in people with hip osteoarthritis

Abnormal hip joint contact forces (HJCF) are considered a primary mechanical contributor to the progression of hip osteoarthritis (OA). Compared to healthy controls, people with hip OA often present with altered muscle activation patterns and greater muscle co-contraction, both of which can influence HJCF. Neuromusculoskeletal (NMS) modelling is non-invasive approach to estimating HJCF, whereby different neural control solutions can be used to estimate muscle forces. Static optimisation, available within the popular NMS modelling software OpenSim, is a commonly used neural control solution, but may not account for an individual’s unique muscle activation patterns and/or co-contraction that are often evident in pathological population. Alternatively, electromyography (EMG)-assisted neural control solutions, available within CEINMS software, have been shown to account for individual activation patterns in healthy people. Nonetheless, their application in people with hip OA, with conceivably greater levels of co-contraction, is yet to be explored. The aim of this study was to compare HJCF estimations using static optimisation (in OpenSim) and EMG-assisted (in CEINMS) neural control solutions during walking in people with hip OA. EMG-assisted neural control solution was more consistent with both EMG and joint moment data than static optimisation, and also predicted significantly higher HJCF peaks (p < 0.001). The EMG-assisted neural control solution also accounted for more muscle co-contraction than static optimisation (p = 0.03), which probably contributed to these higher HJCF peaks. Findings suggest that the EMG-assisted neural control solution may estimate more physiologically plausible HJCF than static optimisation in a population with high levels of co-contraction, such as hip OA.

Sensitivity of Shoulder Musculoskeletal Model Predictions to Muscle-Tendon Properties.

While the sensitivity of estimated muscle forces to muscle-tendon properties is well documented for the lower limbs, little is known about the shoulder and upper limbs. The purpose of this study was to assess the sensitivity of estimated shoulder muscle forces and scapulohumeral joint force to muscle-tendon properties. One healthy male participant performed arm flexions and simulated throwing maneuvers. Kinematics were recorded using intra-cortical pins. Muscle forces were estimated using static optimization with the generic delft shoulder and elbow in OpenSim, and scapulohumeral joint forces were calculated from the estimated forces. Then, variations from -25% to +25% of the nominal values of the tendon slack length, the optimal fiber length, the maximal isometric force, and the pennation angle were applied to the musculoskeletal model to compute affected muscle forces and scapulohumeral joint force. The variations in muscle-tendon properties led to changes up to 9.6N or 174% in the muscle nominal forces. The more sensitive muscles were those that produced the greatest force: the rotator cuff muscles and the prime movers specific to the task. Among the four muscle-tendon properties, the maximal isometric force and the optimal fiber length had the greatest influence on the muscle force variability. Glenohumeral force was slightly influenced by muscle-tendon properties (<8%). Generic models (i.e., those without personalization of muscle-tendon properties) can lead to misinterpretations of muscle force. Efforts should focus on the maximal isometric force and the optimal fiber length of the rotator cuff muscles and prime movers.

A Monte Carlo analysis of muscle force estimation sensitivity to muscle-tendon properties using a Hill-based muscle model

Surface electromyography driven models are desirable for estimating subject-specific muscle forces. However, these models include parameters that come from an array of sources, thus creating uncertainty in the model-estimated force. In this study, we used Monte-Carlo simulations to evaluate the sensitivity of Hill-based model muscle forces to changes in 11 parameters in the muscle-tendon unit morphological properties and in the model force-length and force-velocity relationships. We decomposed the force variability and ranked the sensitivity of the model to the underlying parameters using the Variogram Analysis of Response Surfaces. For the analyzed running experiments and the adopted Hill model structure, our results show that the parameters are separable into four groups, where the parameters in each group have a synergistic contribution to the model global sensitivity. The first group consists of the maximum isometric force and the pennation angle. The second group contains the optimal fiber length, the tendon slack length, the tendon reference strain and the tendon shape factor. The third group contains the width and shape at the extremities of the active contractile element, along with the maximum contraction velocity and the curvature constant in the force-velocity curve. The fourth group consisted only of the force enhancement during eccentric contraction. The first two groups revealed the largest influence on the output force sensitivity. As many input parameters are difficult to measure and impact estimated forces, we propose that model estimates be presented with confidence intervals as well as inter-parameter relationships, to encourage users to explicitly consider the model uncertainty.

Altered Walking and Muscle Patterns Reduce Hip Contact Forces in Individuals With Symptomatic Cam Femoroacetabular Impingement

Background: Cam-type femoroacetabular impingement (FAI) is a causative factor for hip pain and early hip osteoarthritis. Although cam FAI can alter hip joint biomechanics, it is unclear what role muscle forces play and how they affect the hip joint loading. Purpose/Hypothesis: The purpose was to examine the muscle contributions and hip contact forces in individuals with symptomatic cam FAI during level walking. Patients with symptomatic cam FAI would demonstrate different muscle and hip contact forces during gait. Study Design: Controlled laboratory study. Methods: Eighteen patients with symptomatic cam FAI were matched for age and body mass index with 18 control participants. Each participant’s walking kinematics and kinetics were recorded throughout a gait cycle (ipsilateral foot-strike to ipsilateral foot-off) by use of a motion capture system and force plates. Muscle and hip contact forces were subsequently computed by use of a musculoskeletal modeling program and static optimization methods. Results: The FAI group walked slower and with shorter steps, demonstrating reduced joint motions and moments during contralateral foot-strike, compared with the control group. The FAI group showed reduced psoas major (median, 1.1 newtons per bodyweight [N/BW]; interquartile range [IQR], 1.0-1.5 N/BW) and iliacus forces (median, 1.2 N/BW; IQR, 1.0-1.6 N/BW), during contralateral foot-strike, compared with the control group (median, 1.6 N/BW; IQR, 1.3-1.6 N/BW, P = .004; and median, 1.5 N/BW; IQR, 1.3-1.6 N/BW, P = .03, respectively), which resulted in lower hip contact forces in the anterior (P = .026), superior (P = .02), and medial directions (P = .038). The 3 vectors produced a resultant peak force at the anterosuperior aspect of the acetabulum for both groups, with the FAI group demonstrating a substantially lower magnitude. Conclusion: FAI participants altered their walking kinematics and kinetics, especially during contralateral foot-strike, as a protective mechanism, which resulted in reduced psoas major and iliacus muscle force and anterosuperior hip contact force estimations. Clinical Relevance: Limited hip mobility not only is attributed to bone-on-bone impingement, caused by cam morphology, but could be attributed to musculature as well. Not only would the psoas major and iliacus be able to protect the hip joint during flexion-extension, athletic conditioning could further strengthen core muscles for improved hip mobility and pelvic balance.

ESTIMATING FATIGUE LEVEL OF FEMORAL AND GASTROCEMIUS MUSCLES BASED ON SURFACE ELECTROMYOGRAPHY IN TIME AND FREQUENCY DOMAIN

This paper presents a new method for estimating muscle fatigue level based on surface electromyography (EMG) of femoral and gastrocnemius muscles during repetitive motions with various load. The relationship between fatigue level and EMG signals was examined through repetitive movements of the femoral and gastrocnemius muscles with the use of leg extension and squat machines. The fatigue level was based on the maximum voluntary contraction (MVC) levels with various loads. The integrated EMG (IEMG) value and the mean frequency value for each load cycle were obtained through the surface EMG signal. This work presents a global EMG index map by using the new analytical technique based on the relationship between the average IEMG and mean power frequency (MPF) values. The proposed method enables simultaneous estimation of muscle fatigue level and force using real-time EMG signals from the femoral and gastrocnemius muscles.

The influence of maximum isometric muscle force scaling on estimated muscle forces from musculoskeletal models of children with cerebral palsy

BackgroundMusculoskeletal models do not include patient-specific muscle forces but rely on a scaled generic model, with muscle forces left unscaled in most cases. However, to use musculoskeletal simulations to inform clinical decision-making in children with cerebral palsy (CP), inclusion of subject-specific muscle forces is of utmost importance in order to represent each child's compensation mechanisms introduced through muscle weakness. Research aimThe aims of this study were to (i) evaluate if maximum isometric muscle forces (MIMF) in musculoskeletal models of children with CP can be scaled based on strength measurements obtained with a hand-held-dynamometer (HHD), (ii) evaluate the impact of the HHD based scaling approach and previously published MIMF scaling methods on computed muscle forces during gait, and (iii) compare maximum muscle forces during gait between CP and typically developing (TD) children. MethodsStrength and motion capture data of six CP and motion capture data of six TD children were collected. The HHD measurements to obtain hip, knee and ankle muscle strength were simulated in OpenSim and used to modify MIMF of the 2392-OpenSim model. These muscle forces were compared to the MIMF scaled on the child’s body mass and a scaling approach, which included the body mass and muscle-tendon lengths. OpenSim was used to calculate peak muscle forces during gait. ResultsAnkle muscle strength was insufficient to reproduce joint moments during walking when MIMF were scaled based on HHD. During gait, peak hip and knee extensor muscle forces were higher and peak ankle dorsi-flexor forces were lower in CP compared to TD participants. SignificanceHHD measurements can be used to scale MIMF for the hip and knee muscle groups but underestimate the force capacity of the ankle muscle groups during walking. Muscle-tendon-length and mass based scaling methods affected muscle activations but had little influence on peak muscle forces during gait.

O 106 – Influence of ankle’s degree of freedom on muscle force estimation in different simulation environments

The main movements of the ankle joint are dorsi-, plantarflexion (DF/PF), eversion and inversion. In musculoskeletal modelling of walking, the ankle is often simplified, e.g. restricting the degree of freedom (DoF) of the ankle joint, which might lead to altered muscle force estimations. Different simulation environments use different standard anatomical models which complicates comparison between results.

A SEMG-Force Estimation Framework Based on a Fast Orthogonal Search Method Coupled with Factorization Algorithms.

A novel framework based on the fast orthogonal search (FOS) method coupled with factorization algorithms was proposed and implemented to realize high-accuracy muscle force estimation via surface electromyogram (SEMG). During static isometric elbow flexion, high-density SEMG (HD-SEMG) signals were recorded from upper arm muscles, and the generated elbow force was measured at the wrist. HD-SEMG signals were decomposed into time-invariant activation patterns and time-varying activation curves using three typical factorization algorithms including principal component analysis (PCA), independent component analysis (ICA), and nonnegative matrix factorization (NMF). The activation signal of the target muscle was obtained by summing the activation curves, and the FOS algorithm was used to create basis functions with activation signals and establish the force estimation model. Static isometric elbow flexion experiments at three target levels were performed on seven male subjects, and the force estimation performances were compared among three typical factorization algorithms as well as a conventional method for extracting the average signal envelope of all HD-SEMG channels (AVG-ENVLP method). The overall root mean square difference (RMSD) values between the measured forces and the estimated forces obtained by different methods were 11.79 ± 4.29% for AVG-ENVLP, 9.74 ± 3.77% for PCA, 9.59 ± 3.81% for ICA, and 9.51 ± 4.82% for NMF. The results demonstrated that, compared to the conventional AVG-ENVLP method, factorization algorithms could substantially improve the performance of force estimation. The FOS method coupled with factorization algorithms provides an effective way to estimate the combined force of multiple muscles and has potential value in the fields of sports biomechanics, gait analysis, prosthesis control strategy, and exoskeleton devices for assisted rehabilitation.

A musculoskeletal model of the hand and wrist: model definition and evaluation

To improve our understanding on the neuromechanics of finger movements, a comprehensive musculoskeletal model is needed. The aim of this study was to build a musculoskeletal model of the hand and wrist, based on one consistent data set of the relevant anatomical parameters. We built and tested a model including the hand and wrist segments, as well as the muscles of the forearm and hand in OpenSim. In total, the model comprises 19 segments (with the carpal bones modeled as one segment) with 23 degrees of freedom and 43 muscles. All required anatomical input data, including bone masses and inertias, joint axis positions and orientations as well as muscle morphological parameters (i.e. PCSA, mass, optimal fiber length and tendon length) were obtained from one cadaver of which the data set was recently published. Model validity was investigated by first comparing computed muscle moment arms at the index finger metacarpophalangeal (MCP) joint and wrist joint to published reference values. Secondly, the muscle forces during pinching were computed using static optimization and compared to previously measured intraoperative reference values. Computed and measured moment arms of muscles at both index MCP and wrist showed high correlation coefficients (r = 0.88 averaged across all muscles) and modest root mean square deviation (RMSD = 23% averaged across all muscles). Computed extrinsic flexor forces of the index finger during index pinch task were within one standard deviation of previously measured in-vivo tendon forces. These results provide an indication of model validity for use in estimating muscle forces during static tasks.

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.

O 037 – Estimating musculotendon forces in children with cerebral palsy: The importance of the use of electromyography in neuromusculoskeletal modelling

1. Introduction Computational modelling of the neuromusculoskeletal system (NMSS) can potentially provide detailed insight into muscle function to optimize treatment planning and evaluation in cerebral palsy (CP). Commonly, static optimization is used to solve the redundancy problem in estimating muscle forces, assuming, for example, minimization of the muscle activation squared. However, since the primary problem in children with CP is an aberrant motor control [1], it is questionable if using this criterion is applicable in this clinical population. Electromyography (EMG) might be used to further inform optimization procedures and improve model performance. 2. Research question What is gained in using EMG-based NMSS modelling to estimate muscle activations during gait in children with CP compared to static optimization? 3. Methods Five patients with CP participated (11.4 ± 2.7 y, GMFCS I–II). Each patient walked on an instrumented treadmill at comfortable self-selected walking speed, while marker trajectories, ground reaction forces and EMG of eight lower limb muscles were recorded. OpenSim [2] was used to scale a generic model to the lengths of the patients’ segments, using static pose data. Inverse kinematics and inverse dynamics were applied on this model for four randomly selected strides. Four different modeling approaches were used to estimate musculotendon forces. The first two approaches used the static optimization tool in OpenSim to estimate muscle forces, either directly (SO), or after scaling the maximum isometric force of each muscle to body mass (SO_MIF). The third (EA_uncal) and fourth (EA_cal) approach used EMG-assisted modelling, combining EMG data with static optimization (CEINMS software [3]). In EA_cal musculotendon parameters were first calibrated to optimize the matching of EMG-driven moments with inverse dynamics moments [4]. To examine each model’s performance, predicted activations were correlated to activations derived from EMG, and predicted joint moments were correlated to those estimated using inverse dynamics. Correlations were quantified by R^2, and compared between approaches with repeated measures ANOVA. 4. Results Scaling the maximum isometric force did not significantly improve the R^2 for the model’s activations with EMG activations (Fig. 1A). Using an EMG-assisted approach improved the R^2, but not significantly for EA_uncal. EA_cal showed a significantly higher R^2 vs. SO (p = 0.050) and EA_uncal (p = 0.022). Using both EMG-assisted approaches only slightly decreased the tracking of inverse dynamics moments (Fig. 1B), without any significant differences.

O 021 – Does including EMG data in the estimation of muscle forces alter the evaluation of the effect of botulinum toxin injections?

O 021 – Does including EMG data in the estimation of muscle forces alter the evaluation of the effect of botulinum toxin injections?

Robust and accurate decoding of motoneuron behaviour and prediction of the resulting force output.

The spinal alpha motoneuron is the only cell in the human CNS whose discharge can be routinely recorded in humans. We have reengineered motor unit collection and decomposition approaches, originally developed in humans, to measure the neural drive to muscle and estimate muscle force generation in the in vivo cat model. Experimental, computational, and predictive approaches are used to demonstrate the validity of this approach across a wide range of modes to activate the motor pool. The utility of this approach is shown through the ability to track individual motor units across trials, allowing for better predictions of muscle force than the electromyography signal, and providing insights in to the stereotypical discharge characteristics in response to synaptic activation of the motor pool. This approach now allows for a direct link between the intracellular data of single motoneurons, the discharge properties of motoneuron populations, and muscle force generation in the same preparation. The discharge of a spinal alpha motoneuron and the resulting contraction of its muscle fibres represents the functional quantum of the motor system. Recent advances in the recording and decomposition of the electromyographic signal allow for the identification of several tens of concurrently active motor units. These detailed population data provide the potential to achieve deep insights into the synaptic organization of motor commands. Yet most of our understanding of the synaptic input to motoneurons is derived from intracellular recordings in animal preparations. Thus, it is necessary to extend the new electrode and decomposition methods to recording of motor unit populations in these same preparations. To achieve this goal, we use high-density electrode arrays and decomposition techniques, analogous to those developed for humans, to record and decompose the activity of tens of concurrently active motor units in a hindlimb muscle in the in vivo cat. Our results showed that the decomposition method in this animal preparation was highly accurate, with conventional two-source validation providing rates of agreement equal to or superior to those found in humans. Multidimensional reconstruction of the motor unit action potential provides the ability to accurately track the same motor unit across multiple contractions. Additionally, correlational analyses demonstrate that the composite spike train provides better estimates of whole muscle force than conventional estimates obtained from the electromyographic signal. Lastly, stark differences are observed between the modes of activation, in particular tendon vibration produced quantal interspike intervals at integer multiples of the vibration period.