Estimate Muscle Forces Research Articles

Adaptive algorithms: a bibliographic review

The analysis of a research work developed in the company C.V.G CARBONORCA of Venezuela is presented, which has two gas purification plants for the cooking area, designed to purify the gas that comes from the cooking ovens. Each plant is made up of solenoid valves, pneumatic valves, transmitters, process mimic panel and a supervisory system. All these elements are governed by a SIEMENS S5-115U PLC which is in a state of obsolescence, which is why the replacement of these automata by ALLEN BRADLEY ContolLogix automata was designed, in order to guarantee continuity in operations in plant. The research was done with a descriptive design of the field experimental type. A code for each gas treatment plant was obtained in RSLOGIX 5000 v17.00.00 and the update of the database of the supervisory system. The operation of the program was also verified through a simulation of the plant in a supervisory system, the deployment of which was designed for this purpose.
 Keywords: Automation, Modernization, ControlLogix, Supervisory System, Mimic Panel
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Effect of isolated hip abductor fatigue on single-leg landing mechanics and simulated ACL loading

BackgroundAltered movement biomechanics are a risk factor for ACL injury. While hip abductor weakness has been shown to negatively impact landing biomechanics, the role of this musculature and injury risk is not clear. The aim of this musculoskeletal simulation study was to determine the effect of hip abductor fatigue-induced weakness on ACL loading, force production of lower extremity muscles, and lower extremity biomechanics during single-leg landing. MethodsBiomechanical data from ten healthy adults were collected before and after a fatigue protocol and used to derive subject-specific estimates of muscle forces and ACL loading using a 5-degree of freedom (DOF) model. ResultsThere were no significant differences in knee joint angles and ACL loading between pre and post-fatigue. However, there were significant differences, due to fatigue, in lateral trunk flexion angle, total excursion of trunk, muscle forces, and joint moments. ConclusionAltered landing mechanics, due to hip abductor fatigue-induced weakness, may be associated with increased risk of ACL injury during single-leg landings. Clinical assessment or screening of ACL injury risk will benefit from subject-specific musculoskeletal models during dynamic movements. Future study considering the type of the fatigue protocols, cognitive loads, and various tasks is needed to further identify the effect of hip abductor weakness on lower extremity landing biomechanics.

Functional Myology of the Neck in Primates

The neck is often used to study postural and locomotor behaviors across primates. Partially due to the complexity of this 3D system, there has not yet been a quantitative study of comparative nuchal muscle function. In this study, we applied innovative methods to visualize, quantify, and compare the epaxial muscles among primate taxa to characterize the relationship between trunk posture, extreme head turning behaviors, and forelimb-dominated below branch suspensory locomotion and muscle function. DiceCT imaging, 3D muscle fascicle-tracking, and 3D muscle visualization were all used to compare the attachment sites, orientations, and estimates of force for 19 muscles across five species that displayed contrasting postural and locomotor behaviors. To explore estimates of muscle force and 3D orientation of muscle bellies, ternary diagrams were used. Results show that most muscle forces scale with isometry, but musculature varies according to positional behavior and function. Semisuspensory species have a more dorsoventrally oriented cranial bellies of the trapezius muscle than closely related, but non-suspensory taxa. Pronograde primates have more powerful longissimus capitis muscles compared to orthograde primates. Carlito, noted for its pronounced head turning behaviors, has a more transversely oriented and more powerful sternocleidomastoid in addition to more powerful obliquus capitis superior muscle. The latter is also seen in Propithecus. These findings support the hypothesis that estimates of muscle force for certain key neck muscles are related to positional behaviors in primates. Therefore, the bony attachment sites of the muscles that were observed to vary among taxa could aid in understanding locomotor behaviors in extinct primates.

Machine learning for rapid estimation of lower extremity muscle and joint loading during activities of daily living

Joint contact and muscle forces estimated with musculoskeletal modeling techniques offer useful metrics describing movement quality that benefit multiple research and clinical applications. The expensive processing of laboratory data associated with generating these outputs presents challenges to researchers and clinicians, including significant time and expertise requirements that limit the number of subjects typically evaluated. The objective of the current study was to develop and compare machine learning techniques for rapid, data-driven estimation of musculoskeletal metrics from derived gait lab data. OpenSim estimates of patient joint and muscle forces during activities of daily living were simulated using laboratory data from 70 total knee replacement patients and used to develop 4 different machine learning algorithms. Trained machine learning models predicted both trend and magnitude of estimated joint contact (mean correlation coefficients ranging from 0.93 to 0.94 during gait) and muscle forces (mean correlation coefficients ranging from 0.83 to 0.91 during gait) based on anthropometrics, ground reaction forces, and joint angle data. Patient mechanics were accurately predicted by recurrent neural networks, even after removing dependence on key subsets of predictor features. The ability to quickly estimate patient mechanics from derived measurements of movement has the potential to broaden the impact of musculoskeletal modeling by enabling faster assessment in both clinical and research settings.

The effect of multiplanar loading on the intradiscal pressure of the whole human spine: systematic review and meta-analysis.

For spinal load and muscle force estimation as well as for numerical model and experimental setup validation, data on human intradiscal pressure are essential. Therefore, the aim of the present meta-analysis was to summarise all in vitro measurements of human intradiscal pressure performed under defined boundary conditions, i.e. without external loading (intrinsic pressure), under axial loading (compression, traction, shear) and under single-planar bending loading (flexion, extension, lateral bending, axial rotation). Data were evaluated based on segmental level and normalised to force and moment. Regression analysis was performed to investigate coefficients of determination and statistical significance of relationships between intradiscal pressure and segmental level for the single loading conditions. 35 studies fulfilled the inclusion criteria, from which a total of 451 data points were collected for the meta-analysis. High coefficients of determination were found in axial compression (r2 = 0.875) and flexion (r2 = 0.781), while being low for intrinsic pressure (r2 = 0.266) and lateral bending (r2 = 0.385), all showing significant regression fitting (p < 0.01). Intradiscal pressure decreases from the upper cervical spine to the sacrum in all loading conditions, considering the same amount of loading for all segmental levels, while the intrinsic pressure exhibits a minimum of the regression curve in the mid-thoracic spine. Apart from its potential for numerical and experimental model validation, this dataset may help to understand the load distribution along the human spine.

Real-Time and Dynamically Consistent Estimation of Muscle Forces Using a Moving Horizon EMG-Marker Tracking Algorithm-Application to Upper Limb Biomechanics.

Real-time biofeedback of muscle forces should help clinicians adapt their movement recommendations. Because these forces cannot directly be measured, researchers have developed numerical models and methods informed by electromyography (EMG) and body kinematics to estimate them. Among these methods, static optimization is the most computationally efficient and widely used. However, it suffers from limitation, namely: unrealistic joint torques computation, non-physiological muscle forces estimates and inconsistent for motions inducing co-contraction. Forward approaches, relying on numerical optimal control, address some of these issues, providing dynamically consistent estimates of muscle forces. However, they result in a high computational cost increase, apparently disqualifying them for real-time applications. However, this computational cost can be reduced by combining the implementation of a moving horizon estimation (MHE) and advanced optimization tools. Our objective was to assess the feasibility and accuracy of muscle forces estimation in real-time, using a MHE. To this end, a 4-DoFs arm actuated by 19 Hill-type muscle lines of action was modeled for simulating a set of reference motions, with corresponding EMG signals and markers positions. Excitation- and activation-driven models were tested to assess the effects of model complexity. Four levels of co-contraction, EMG noise and marker noise were simulated, to run the estimator under 64 different conditions, 30 times each. The MHE problem was implemented with three cost functions: EMG-markers tracking (high and low weight on markers) and marker-tracking with least-squared muscle excitations. For the excitation-driven model, a 7-frame MHE was selected as it allowed the estimator to run at 24 Hz (faster than biofeedback standard) while ensuring the lowest RMSE on estimates in noiseless conditions. This corresponds to a 3,500-fold speed improvement in comparison to state-of-the-art equivalent approaches. When adding experimental-like noise to the reference data, estimation error on muscle forces ranged from 1 to 30 N when tracking EMG signals and from 8 to 50 N (highly impacted by the co-contraction level) when muscle excitations were minimized. Statistical analysis was conducted to report significant effects of the problem conditions on the estimates. To conclude, the presented MHE implementation proved to be promising for real-time muscle forces estimation in experimental-like noise conditions, such as in biofeedback applications.

Determination of the correlation between muscle forces obtained from OpenSim and muscle activities obtained from electromyography in the elderly.

Measurement of muscle forces related to aging can help to better identify the gait impairment mechanisms in the elderly. To this end, musculoskeletal modeling has been developed to estimate muscle forces. This study aimed to check the validity of OpenSim modeling (i.e., computed muscle control) approach in elderly subjects. Kinematic and kinetic data and Electromyography (EMG) signals for four different muscles were collected in nine healthy elderly males during walking. Dynamic simulation was done within OpenSim. Correlation analysis was performed to quantitatively compare the maximum estimated muscle forces with maximum measured muscle activities during the first double limb support, single limb support, and the second double limb support phases. The area-time plots of OpenSim and EMG data during gait cycle were obtained for qualitative assessment. In quantitative assessment, a low to moderate correlation was observed for the peak of muscle force and muscle activation of four muscles during sub phases of gait. The muscle forces pattern from OpenSim was found to be relatively similar to the muscle activity pattern from EMG especially for Gastrocnemius Medialis. A low to moderate consistency between OpenSim and EMG in the elderly can be explained by using a single mathematical estimation approach.

Decoding Muscle Force From Motor Unit Firings Using Encoder-Decoder Networks.

Appropriate interpretation of motor unit (MU) activities after surface EMG (sEMG) decomposition is a key factor to decode motor intentions in a noninvasive and physiologically meaningful way. However, there are great challenges due to the difficulty in cross-trial MU tracking and unavoidable loss of partial MU information resulting from incomplete decomposition. In light of these issues, this study presents a novel framework for interpreting MU activities and applies it to decode muscle force. The resulting MUs were clustered and classified into different categories by characterizing their spatially distributed firing waveforms. The process served as a general MU tracking method. On this basis, after transferring the MU firing trains to twitch force trains by a twitch force model, a deep network was designed to predict the normalized force. In addition, MU category distribution was examined to calibrate the actual force level, while functions of some unavailable MUs were compensated. To investigate the effectiveness of this framework, high-density sEMG signals were recorded using an 8×8 electrode array from the abductor pollicis brevis muscles of eight subjects, while thumb abduction force was measured. The proposed method outperformed three common methods ( ) yielding the lowest root mean square deviation of 6.68% ± 1.29% and the highest fitness ( R2 ) of 0.94 ± 0.04 between the predicted force and the actual force. This study offers a valuable, computational solution for interpreting individual MU activities, and its effectiveness was confirmed in muscle force estimation.

Evaluation of Synergy Extrapolation for Predicting Unmeasured Muscle Excitations from Measured Muscle Synergies.

Electromyography (EMG)-driven musculoskeletal modeling relies on high-quality measurements of muscle electrical activity to estimate muscle forces. However, a critical challenge for practical deployment of this approach is missing EMG data from muscles that contribute substantially to joint moments. This situation may arise due to either the inability to measure deep muscles with surface electrodes or the lack of a sufficient number of EMG channels. Muscle synergy analysis (MSA) is a dimensionality reduction approach that decomposes a large number of muscle excitations into a small number of time-varying synergy excitations along with time-invariant synergy weights that define the contribution of each synergy excitation to all muscle excitations. This study evaluates how well missing muscle excitations can be predicted using synergy excitations extracted from muscles with available EMG data (henceforth called “synergy extrapolation” or SynX). The method was evaluated using a gait data set collected from a stroke survivor walking on an instrumented treadmill at self-selected and fastest-comfortable speeds. The evaluation process started with full calibration of a lower-body EMG-driven model using 16 measured EMG channels (collected using surface and fine wire electrodes) per leg. One fine wire EMG channel (either iliopsoas or adductor longus) was then treated as unmeasured. The synergy weights associated with the unmeasured muscle excitation were predicted by solving a nonlinear optimization problem where the errors between inverse dynamics and EMG-driven joint moments were minimized. The prediction process was performed for different synergy analysis algorithms (principal component analysis and non-negative matrix factorization), EMG normalization methods, and numbers of synergies. SynX performance was most influenced by the choice of synergy analysis algorithm and number of synergies. Principal component analysis with five or six synergies consistently predicted unmeasured muscle excitations the most accurately and with the greatest robustness to EMG normalization method. Furthermore, the associated joint moment matching accuracy was comparable to that produced by initial EMG-driven model calibration using all 16 EMG channels per leg. SynX may facilitate the assessment of human neuromuscular control and biomechanics when important EMG signals are missing.

Muscle Force Estimation Based on Neural Drive Information From Individual Motor Units.

Estimation of muscle contraction force based on the macroscopic feature of surface electromyography (SEMG) has been widely reported, but the use of microscopic neural drive information has not been thoroughly investigated. In this study, a novel method is proposed to process individual motor unit (MU) activities (firing sequences and action potential waveforms) derived from the decomposition of high density SEMG (HD-SEMG), and it is applied to muscle force estimation. In the proposed method, a supervised machine learning approach was conducted to determine the twitch force of each MU according to its action potential waveforms, which enables separate calculation of every MU's contribution to force. Thus, the muscle force was predicted through a physiologically meaningful muscle force model. In the experiment, HD-SEMG data were recorded from the abductor pollicis brevis muscles of eight healthy subjects during their performance of thumb abduction with the force increasing gradually from zero to four force levels (10%, 20%, 30%, 40% of the maximal voluntary contraction), while the true muscle force was measured simultaneously. When the proposed method was used, the root mean square difference (RMSD) of the error of the estimated force with respect to the measured force was reported to be 8.3% ± 2.8%. The proposed method also significantly outperformed the other four common methods for force estimation (RMSD: from 11.7% to 20%, ), demonstrating its effectiveness. This study offers a useful tool for exploiting the neural drive information towards muscle force estimation with improved precision. The proposed method has wide applications in precise motor control, sport and rehabilitation medicine.

Upper body estimation of muscle forces, muscle states, and joint motion using an extended Kalman filter

This study synthesises modelling techniques and dynamic state estimation techniques for the simultaneous estimation of the muscle states, muscle forces, and joint motion states of a dynamic human arm model. The estimator considers both muscle dynamics and motion dynamics. The arm model has two joints and six muscles and contains dynamics both of the muscles and of the motion. We develop an optimally tuned extended Kalman filter using noisy measurements of joint angles with standard deviation 2.87 °, of joint velocities with standard deviation 6.9 °/s, and of muscle activations with standard deviation 10% of their peak values, and then simultaneously estimate joint angles, joint velocities, muscle forces, joint moments, and muscle states. The standard deviations of estimation errors (SDEE) are no more than 0.07° for joint angles, 1 °/s for joint velocities, 0.6 mm for muscle–tendon lengths, and 0.1 Nm for joint torques. The results are compared with a previously developed static optimisation method, and verify the effectiveness of the proposed estimator in providing lower SDEE for both muscle and motion dynamics of the human arm model compared to the static optimisation method.

Effect of Expertise on Shoulder and Upper Limb Kinematics, Electromyography, and Estimated Muscle Forces During a Lifting Task.

To highlight the working strategies used by expert manual handlers compared with novice manual handlers, based on recordings of shoulder and upper limb kinematics, electromyography (EMG), and estimated muscle forces during a lifting task. Novice workers involved in assembly, manual handling, and personal assistance tasks are at a higher risk of upper limb musculoskeletal disorders (MSDs). However, few studies have investigated the effect of expertise on upper limb exposure during workplace tasks. Sixteen experts in manual handling and sixteen novices were equipped with 10 electromyographic electrodes to record shoulder muscle activity during a manual handling task consisting of lifting a box (8 or 12 kg), instrumented with three six-axis force sensors, from hip to eye level. Three-dimensional trunk and upper limb kinematics, hand-to-box contact forces, and EMG were recorded. Then, joint contributions, activation levels, and muscle forces were calculated and compared between groups. Sternoclavicular-acromioclavicular joint contributions were higher in experts at the beginning of the movement, and in novices at the end, whereas the opposite was observed for the glenohumeral joint. EMG activation levels were 37% higher for novices but predicted muscle forces were higher in experts. This study highlights significant differences between experts and novices in shoulder kinematics, EMG, and muscle forces; hence, providing effective work guidelines to ensure the development of a safe handling strategy is important. Shoulder kinematics, EMG, and muscle forces could be used as ergonomic tools to identify inappropriate techniques that could increase the prevalence of shoulder injuries.

12 Degrees of Freedom Muscle Force Driven Fibril-Reinforced Poroviscoelastic Finite Element Model of the Knee Joint.

Accurate knowledge of the joint kinematics, kinetics, and soft tissue mechanical responses is essential in the evaluation of musculoskeletal (MS) disorders. Since in vivo measurement of these quantities requires invasive methods, musculoskeletal finite element (MSFE) models are widely used for simulations. There are, however, limitations in the current approaches. Sequentially linked MSFE models benefit from complex MS and FE models; however, MS model's outputs are independent of the FE model calculations. On the other hand, due to the computational burden, embedded (concurrent) MSFE models are limited to simple material models and cannot estimate detailed responses of the soft tissue. Thus, first we developed a MSFE model of the knee with a subject-specific MS model utilizing an embedded 12 degrees of freedom (DoFs) knee joint with elastic cartilages in which included both secondary kinematic and soft tissue deformations in the muscle force estimation (inverse dynamics). Then, a muscle-force-driven FE model with fibril-reinforced poroviscoelastic cartilages and fibril-reinforced poroelastic menisci was used in series to calculate detailed tissue mechanical responses (forward dynamics). Second, to demonstrate that our workflow improves the simulation results, outputs were compared to results from the same FE models which were driven by conventional MS models with a 1 DoF knee, with and without electromyography (EMG) assistance. The FE model driven by both the embedded and the EMG-assisted MS models estimated similar results and consistent with experiments from literature, compared to the results estimated by the FE model driven by the MS model with 1 DoF knee without EMG assistance.

Considerations on the human Achilles tendon moment arm for in vivo triceps surae muscle\u2013tendon unit force estimates

Moment arm-angle functions (MA-a-functions) are commonly used to estimate in vivo muscle forces in humans. However, different MA-a-functions might not only influence the magnitude of the estimated muscle forces but also change the shape of the muscle’s estimated force-angle relationship (F-a-r). Therefore, we investigated the influence of different literature based Achilles tendon MA-a-functions on the triceps surae muscle–tendon unit F-a-r. The individual in vivo triceps torque–angle relationship was determined in 14 participants performing maximum voluntary fixed-end plantarflexion contractions from 18.3° ± 3.2° plantarflexion to 24.2° ± 5.1° dorsiflexion on a dynamometer. The resulting F-a-r were calculated using 15 literature-based in vivo Achilles tendon MA-a-functions. MA-a-functions affected the F-a-r shape and magnitude of estimated peak active triceps muscle–tendon unit force. Depending on the MA-a-function used, the triceps was solely operating on the ascending limb (n = 2), on the ascending limb and plateau region (n = 12), or on the ascending limb, plateau region and descending limb of the F-a-r (n = 1). According to our findings, the estimated triceps muscle–tendon unit forces and the shape of the F-a-r are highly dependent on the MA-a-function used. As these functions are affected by many variables, we recommend using individual Achilles tendon MA-a-functions, ideally accounting for contraction intensity-related changes in moment arm magnitude.

Muscle co-contraction in an upper limb musculoskeletal model: EMG-assisted vs. standard load-sharing

Estimation of muscle forces in over-actuated musculoskeletal models involves optimal distributions of net joint moments among muscles by a standard load-sharing scheme (SLS). Given that co-contractions of antagonistic muscles are counterproductive in the net joints moments, SLS might underestimate the co-contractions. Muscle co-contractions play crucial roles in stability of the glenohumeral (GH) joint. The aim of this study was to improve estimations of muscle co-contractions by incorporating electromyography (EMG) data into an upper limb musculoskeletal model. To this end, the model SLS was modified to develop an EMG-assisted load-sharing scheme (EALS). EMG of fifteen muscles were measured during arm flexion and abduction on a healthy subject and fed into the model. EALS was compared to SLS in terms of muscle forces, GH joint reaction force, and a stability ratio defined to quantify the GH joint stability. The results confirmed that EALS estimated higher muscle co-contractions compared to the SLS (e.g., above 50 N higher forces for both triceps long and biceps long during arm flexion).

A Procedure to Define Customized Musculoskeletal Models for the Analysis of the Crutch-Orthosis-Assisted Gait of Spinal Cord Injured Subjects.

Subjects suffering from spinal cord injury with lower extremity impairment generally use a wheelchair to move. However, some of them are capable of walking with the help of orthoses and crutches. Standing up and walking regularly have huge benefits for the general health state of these subjects, since it reduces the negative consequences of sedentarism. Therefore, achieving adherence to assisted gait is important, but there is a risk of abandoning due to several issues such as pain, fatigue, or very low speed, which can make the subject return to solely use the wheelchair. Musculoskeletal models can provide estimations of muscular forces and activations, which in turn enable to calculate magnitudes such as joint reactions, energetic cost, and bone stress and strain. These magnitudes can serve to evaluate the impact of assisted gait in the subject's health and to assess the likelihood of adherence. Moreover, they can be used as indicators to compare different assistive devices for a particular subject. As every spinal cord-injured (SCI) subject represents a different case, a procedure to define customized musculoskeletal models for the crutch-orthosis-assisted gait of SCI subjects is proposed in this paper. Issues such as selection of muscles and integration of models of trunk, upper and lower extremities, and assistive devices (crutches and orthoses) are addressed. An inverse-dynamics-based physiological static optimization method that takes into account muscle dynamics at low computational cost is applied to obtain estimates of muscle forces and joint reactions. The method is experimentally validated by electromyography in a case study.

Mechanical properties of muscle and tendon at high strain rate in sprinters.

The aim of the present study was to compare the mechanical properties of muscles and tendons at high strain rates between sprinters and untrained men. Fifteen sprinters and 18 untrained men participated in this study. Active muscle stiffness of the medial gastrocnemius muscle was calculated according to changes in the estimated muscle force and fascicle length during fast stretching at five different angular velocities (100, 200, 300, 500, and 600 deg·s−1) after submaximal isometric contractions. Stiffness and hysteresis of tendon structures were measured during ramp and ballistic contractions. Active muscle stiffness at 500 deg·s−1 (p = .070) and 600 deg·s−1 (p = .041) was greater in sprinters than untrained men, whereas no differences in those at 100, 200, and 300 deg·s−1 were found between the two groups. There were no differences in stiffness or hysteresis of tendon structures measured during ramp and ballistic contractions between the two groups. These results suggest that, for sprinters, greater active muscle stiffness at a high angular velocity is caused by exercising with a high angular velocity that is typical of their training.

Estimating Muscle Forces for Breast Cancer Survivors During Functional Tasks.

Breast cancer survivors have known scapular kinematic alterations that may be related to the development of secondary morbidities. A measure of muscle activation would help understand the mechanisms behind potential harmful kinematics. The purpose of this study was to define muscle force strategies in breast cancer survivors. Shoulder muscle forces during 6 functional tasks were predicted for 25 breast cancer survivors (divided by impingement pain) and 25 controls using a modified Shoulder Loading Analysis Module. Maximum forces for each muscle were calculated, and 1-way analysis of variance (P < .05) was used to identify group differences. The differences between maximum predicted forces and maximum electromyography were compared with repeated-measures analysis of variance (P < .05) to evaluate the success of the model predictions. Average differences between force predictions and electromyography ranged from 7.3% to 31.6% but were within the range of previously accepted differences. Impingement related pain in breast cancer survivors is associated with increased force of select shoulder muscles. Both pectoralis major heads, upper trapezius, and supraspinatus peak forces were higher in the pain group across all tasks. These force prediction differences are also associated with potentially harmful kinematic strategies, providing a direction for possible rehabilitation strategies.

Muscle contributions to maximal single-leg forward braking and backward acceleration in elite athletes

Abrupt deceleration is a common practice in several sports, where sudden changes of direction are needed to reach the highest performance level. When inappropriately performed, these actions can impose excessive mechanical loads at the lower limb joints, specifically at the knee and ankle joints, usually associated with increased risk of injury. This work aims to estimate muscle forces and muscle contributions to the acceleration of the center of mass during a rapid maximal single-leg forward braking and backward acceleration task. Fourteen elite male injury-free indoor-sports athletes participated in this work. Scaled generic musculoskeletal models, consisting of 12 segments, 23 degrees of freedom, and 92 muscle-tendon actuators were used in OpenSim software. Due to the nature of the musculoskeletal system, all muscles are considered when joint and segment positions, velocities, and accelerations are calculated, resulting in muscles acting to accelerate joints it does not span. The knowledge of muscle interaction during this multijoint task is important and was achieved through an induced acceleration analysis. The vasti (−9.18 ± 2.09 and −7.63 ± 1.33 N/Kg) were the main contributors to the centre of mass deceleration profile along the anterior/posterior direction, aided by the soleus muscle (9.72 ± 2.35 and 9.62 ± 2.07 N/Kg), which counteracted most of the effects applied by gravity along the vertical direction, during both phases. This study provides a computational approach to quantify the dynamical interactions between muscles and joints during an abrupt anterior/posterior deceleration task, thus giving robust and insightful indicators that can be implemented in injury prevention protocols.

Effect of angular velocity on active muscle stiffness in vivo

We previously reported that active muscle stiffness could be evaluated in vivo. However, we were not able to investigate active muscle stiffness as more than 250 deg·s−1 due to the limitation of the torque motor of dynamometer. The aim of the present study was to investigate the effect of angular velocities (including higher angular velocities of more than 250 deg·s−1) on active muscle stiffness. Eighteen males volunteered for this study. Active muscle stiffness of the medial gastrocnemius muscle was calculated according to changes in the estimated muscle force and fascicle length during fast lengthening at five different angular velocities (100, 200, 300, 500, and 600 deg·s−1). Electromyographic activities of the lateral gastrocnemius muscle (LG) and soleus muscle (SOL) were evaluated over two different phases: before the stretch (mEMGa) and after the stretch (mMEGb). Active muscle stiffness was higher at 300 than at 100 deg·s−1, but decreased as the angular velocity increased from 300 to 600 deg·s−1. There were no differences in mEMGa and mEMGb values among the five angular velocities, whereas mEMGb values were higher than mEMGa for all angular velocities. In conclusion, active muscle stiffness was highest at 300 deg·s−1 and decreased at both slower and faster angular velocities.