Muscle Force Patterns Research Articles

Increasing load carriage and running speed differentially affect the magnitude, variability and coordination patterns of muscle forces

The study aims to investigate the effects of different loads and speed during running on inter- and intra-individual muscle force amplitudes, variabilities and coordination patterns. Nine healthy participants ran on an instrumentalized treadmill with an empty weight vest at two velocities (2.6 m/s and 3.3 m/s) or while carrying three different loads (4.5, 9.1, 13.6 kg) at 2.6 m/s while kinematics and kinetics were synchronously recorded. The major lower limb muscle forces were estimated using a musculoskeletal model. Muscle force amplitudes and variability, as well as coordination patterns were compared at the group and at the individual level using respectively statistical parametric mapping and covariance matrices combined with multidimensional scaling. Increasing the speed or the load during running increased most of the muscle force amplitudes (p < 0.01). During the propulsion phase, increasing the load increased muscle force variabilities around the ankle joint (modification of standard deviation up to 5% of body weight (BW), p < 0.05) while increasing the speed decreased variability for almost all the muscle forces (up to 10% of BW, p < 0.05). Each runner has a specific muscle force coordination pattern signature regardless of the different experimental conditions (p < 0.05). Yet, this individual pattern was slightly adapted in response to a change of speed or load (p < 0.05). Our results suggest that adding load increases the amplitude and variability of muscle force, but an increase in running speed decreases the variability. These findings may help improve the design of military or trail running training programs and injury rehabilitation by progressively increasing the mechanical load on anatomical structures.

Muscle Force Patterns in Lower Extremity Muscles for Elite Discus Throwers, Javelin Throwers and Shot-Putters - A Case Study.

Optimal release variables, as well as the kinematics and kinetics of athletes, are crucial for the maximization of throwing distance in athletics. Mathematical models and simulations allow throwing techniques to be studied. However, muscle force patterns and the contribution of specific muscle groups in athletics throwing events are not well understood and require detailed research. In this study, important variables of the muscle force generated during the javelin, discus and shot put events were determined using OpenSim software. Musculoskeletal simulations were carried out based on kinematic and kinetic data collected using the Vicon system and Kistler plates with the help of nine top Polish athletes (three in each event). OpenSim software was used to calculate muscle forces and joint velocities. For each discipline, it was found that the main muscle groups involved in the throwing movement were better at distinguishing throwers than joint velocities. The contribution of right ankle plantar flexors at the beginning of the final acceleration phase as well as left hip extensors at the end of the final acceleration phase was given special attention. This work provides a better understanding of the techniques used in athletics throws. Musculoskeletal simulations of throwing styles might help coaches analyze the techniques of individual athletes, resulting in better adjustment of training programmes and injury prevention protocols.

Muscles Cooperation Analysis Using Akaike Information Criteria for Anterior Cruciate Ligament Injury Prevention.

In this paper, we propose the analysis method for finding out the similarity of the muscle force patterns to mine the risk factor of the anterior cruciate ligament (ACL) injury. Akaike information criteria (AIC) under the assumption of the auto-regression model is adapted to analyze the similarities of muscle force patterns in time-series. The difference of AIC values between 2 muscles is considered to be the distance between 2 muscle force patterns and the dexterity of the maneuver is expected to be discussed. We measured drop vertical jump (DVJ) and use the data around the contact timing of whom hadn't had ACL injury experiments. The results showed that we could successfully calculate AIC distance according to the similarity of the time-series data pattern and it can be useful to discuss one's dexterity of controlling body maneuvers soon after contact timing of DVJ motion.

Hip joint muscle forces during gait in patients with femoroacetabular impingement syndrome are associated with patient reported outcomes and cartilage composition

Femoroacetabular impingement syndrome (FAIS) consists of abnormal hip joint morphology and pain during activities of daily living. Abnormal gait mechanics and potentially abnormal muscle forces within FAI patients leads to articular cartilage damage. Therefore, there is a necessity to understand the effects of FAI on hip joint muscle forces during gait and the link between muscle forces, patient reported outcomes (PRO) and articular cartilage health. The purposes of this study were to assess: (1) hip muscle forces between FAI patients and healthy controls and (2) the associations between hip muscle forces with PRO and cartilage composition (T1ρ/T2 mapping) within FAI patients. Musculoskeletal simulations were used to estimate peak muscle forces during the stance phase of gait in 24 FAI patients and 24 healthy controls. Compared to controls, FAI patients ambulated with lower vasti (30% body-weight, p = 0.01) and higher sartorius (4.0% body-weight, p < 0.01) forces. Within FAI patients, lower peak gluteus medius, gluteus minimus, sartorius and iliopsoas forces were associated with worse hip joint pain and function (R = 0.43–0.70, p = 0–0.04), while lower muscle forces were associated with increased T1ρ and T2 values (i.e. altered cartilage composition) within the hip joint cartilage (R = −0.44 to −0.58, p = 0.006–0.05). Although FAI patients demonstrate abnormal muscle forces, it is unknown whether or not these altered muscle force patterns are associated with pain avoidance or weak musculature. Further investigation is required in order to better understand the effects of FAI on hip joint muscle forces and the associations with hip joint cartilage degeneration.

An Analytical Approach to Posture-Dependent Muscle Force and Muscle Activation Patterns.

Personalized training by taking into account individual anatomy to improve performance is a research frontier. In this paper, we first introduce an analytical method to study the pattern of changes in muscle forces as a function of posture. Our method is also able to analyze variation of maximal muscle force and muscle activation values (in various postures) as a result of posture-dependent changes in moment arms. This method also helps us evaluate the utility of person specific training. It also provides us with model based approximations for activation and muscle force patterns during different motions without a need for subject recordings, which enables athletes to have a better understanding of how each muscle contributes during each posture, in a fast and efficient way. Second, we analyze the results of this method for a simple squat move. Our results show that both maximal muscle force and muscle activation values have variable sensitivity to the moment arm values for different postures and muscles. It suggests that individually modified training plans could likely improve performance for some sets of movements.

A general-purpose framework to simulate musculoskeletal system of human body: using a motion tracking approach

Computation of muscle force patterns that produce specified movements of muscle-actuated dynamic models is an important and challenging problem. This problem is an undetermined one, and then a proper optimization is required to calculate muscle forces. The purpose of this paper is to develop a general model for calculating all muscle activation and force patterns in an arbitrary human body movement. For this aim, the equations of a multibody system forward dynamics, which is considered for skeletal system of the human body model, is derived using Lagrange–Euler formulation. Next, muscle contraction dynamics is added to this model and forward dynamics of an arbitrary musculoskeletal system is obtained. For optimization purpose, the obtained model is used in computed muscle control algorithm, and a closed-loop system for tracking desired motions is derived. Finally, a popular sport exercise, biceps curl, is simulated by using this algorithm and the validity of the obtained results is evaluated via EMG signals.

Neural Networks for Muscle Forces Prediction in Cycling

This paper documents the research towards the development of a system based on Artificial Neural Networks to predict muscle force patterns of an athlete during cycling. Two independent inverse problems must be solved for the force estimation: evaluation of the kinematic model and evaluation of the forces distribution along the limb. By solving repeatedly the two inverse problems for different subjects and conditions, a training pattern for an Artificial Neural Network was created. Then, the trained network was validated against an independent validation set, and compared to evaluate agreement between the two alternative approaches using Bland-Altman method. The obtained neural network for the different test patterns yields a normalized error well below 1% and the Bland-Altman plot shows a considerable correlation between the two methods. The new approach proposed herein allows a direct and fast computation for the inverse dynamics of a cyclist, opening the possibility of integrating such algorithm in a real time environment such as an embedded application.

Pneumatically powered robotic exercise device to induce a specific force profile in target lower extremity muscles

SUMMARYThe goal of this research is to establish a methodology to actively control a pneumatically driven robotic device that can induce specific muscle force patterns in target muscles during a subject's voluntary movement. In this paper, the generation of constant forces in the rectus femoris muscle throughout the knee extension, i.e., isotonic contractions, was studied. Due to a highly nonlinear nature of mapping the joint torque to muscle force, a simple application of constant torques to the knee joint would not realize isotonic contractions. The proposed robotic exercise accounted for nonlinear moment arms of muscles as functions of joint angles and nonlinear coordination of multiple muscles in the neuromuscular system to accomplish individual muscle control. A pneumatically powered one degree-of-freedom device that can impose active force feedback control has been designed and built. An exercise-planning algorithm has been developed that involved a musculoskeletal model of the lower body, and the dynamics of a pneumatic actuator. Five constant force profiles were tested for 20 healthy volunteers and electromyographic signals were collected while the device was applying calculated force profiles.

A real-time system for biomechanical analysis of human movement and muscle function

Mechanical analysis of movement plays an important role in clinical management of neurological and orthopedic conditions. There has been increasing interest in performing movement analysis in real-time, to provide immediate feedback to both therapist and patient. However, such work to date has been limited to single-joint kinematics and kinetics. Here we present a software system, named human body model (HBM), to compute joint kinematics and kinetics for a full body model with 44 degrees of freedom, in real-time, and to estimate length changes and forces in 300 muscle elements. HBM was used to analyze lower extremity function during gait in 12 able-bodied subjects. Processing speed exceeded 120 samples per second on standard PC hardware. Joint angles and moments were consistent within the group, and consistent with other studies in the literature. Estimated muscle force patterns were consistent among subjects and agreed qualitatively with electromyography, to the extent that can be expected from a biomechanical model. The real-time analysis was integrated into the D-Flow system for development of custom real-time feedback applications and into the gait real-time analysis interactive lab system for gait analysis and gait retraining.Electronic supplementary materialThe online version of this article (doi:10.1007/s11517-013-1076-z) contains supplementary material, which is available to authorized users.

Computational tools for calculating alternative muscle force patterns during motion: A comparison of possible solutions

Comparing the available electromyography (EMG) and the related uncertainties with the space of muscle forces potentially driving the same motion can provide insights into understanding human motion in healthy and pathological neuromotor conditions. However, it is not clear how effective the available computational tools are in completely sample the possible muscle forces. In this study, we compared the effectiveness of Metabolica and the Null-Space algorithm at generating a comprehensive spectrum of possible muscle forces for a representative motion frame. The hip force peak during a selected walking trial was identified using a lower-limb musculoskeletal model. The joint moments, the muscle lever arms, and the muscle force constraints extracted from the model constituted the indeterminate equilibrium equation at the joints. Two spectra, each containing 200,000 muscle force samples, were calculated using Metabolica and the Null-Space algorithm. The full hip force range was calculated using optimization and compared with the hip force ranges derived from the Metabolica and the Null-Space spectra. The Metabolica spectrum spanned a much larger force range than the NS spectrum, reaching 811N difference for the gluteus maximus intermediate bundle. The Metabolica hip force range exhibited a 0.3—0.4 BW error on the upper and lower boundaries of the full hip force range (3.4—11.3 BW), whereas the full range was imposed in the NS spectrum. The results suggest that Metabolica is well suited for exhaustively sample the spectrum of possible muscle recruitment strategy. Future studies will investigate the muscle force range in healthy and pathological neuromotor conditions.

Shaping the dynamics of a bidirectional neural interface.

Progress in decoding neural signals has enabled the development of interfaces that translate cortical brain activities into commands for operating robotic arms and other devices. The electrical stimulation of sensory areas provides a means to create artificial sensory information about the state of a device. Taken together, neural activity recording and microstimulation techniques allow us to embed a portion of the central nervous system within a closed-loop system, whose behavior emerges from the combined dynamical properties of its neural and artificial components. In this study we asked if it is possible to concurrently regulate this bidirectional brain-machine interaction so as to shape a desired dynamical behavior of the combined system. To this end, we followed a well-known biological pathway. In vertebrates, the communications between brain and limb mechanics are mediated by the spinal cord, which combines brain instructions with sensory information and organizes coordinated patterns of muscle forces driving the limbs along dynamically stable trajectories. We report the creation and testing of the first neural interface that emulates this sensory-motor interaction. The interface organizes a bidirectional communication between sensory and motor areas of the brain of anaesthetized rats and an external dynamical object with programmable properties. The system includes (a) a motor interface decoding signals from a motor cortical area, and (b) a sensory interface encoding the state of the external object into electrical stimuli to a somatosensory area. The interactions between brain activities and the state of the external object generate a family of trajectories converging upon a selected equilibrium point from arbitrary starting locations. Thus, the bidirectional interface establishes the possibility to specify not only a particular movement trajectory but an entire family of motions, which includes the prescribed reactions to unexpected perturbations.

Development of a comprehensive musculoskeletal model of the shoulder and elbow

The Delft Shoulder and Elbow Model (DSEM), a musculoskeletal model of the shoulder and elbow has been extensively developed since its introduction in 1994. Extensions cover both model structures and anatomical data focusing on the addition of an elbow part and muscle architecture parameters. The model was also extended with a new inverse-dynamics optimization cost function and combined inverse-forward-dynamics models. This study is an update on the developments of the model over the last decade including a qualitative validation of the different simulation architectures available in the DSEM. To validate the model, a dynamic forward flexion motion was performed by one subject, of which the motion data and surface EMG-signals of 12 superficial muscles were measured. Patterns of the model-predicted relative muscle forces were compared with their normalized EMG-signals. Results showed relatively good agreement between forces and EMG (mean correlation coefficient of 0.66). However, for some cases, no force was predicted while EMG activity had been measured (false-negatives). The DSEM has been used and has the potential to be used in a variety of clinical and biomechanical applications.Electronic supplementary materialThe online version of this article (doi:10.1007/s11517-011-0839-7) contains supplementary material, which is available to authorized users.

A mass–length scaling law for modeling muscle strength in the lower limb

Musculoskeletal computer models are often used to study muscle function in children with and without impaired mobility. Calculations of muscle forces depend in part on the assumed strength of each muscle, represented by the peak isometric force parameter, which is usually based on measurements obtained from cadavers of adult donors. The aim of the present study was twofold: first, to develop a method for scaling lower-limb peak isometric muscle forces in typically-developing children; and second, to determine the effect of this scaling method on model calculations of muscle forces obtained for normal gait. Muscle volumes were determined from magnetic resonance (MR) images obtained from ten children aged from 7 to 13 yr. A new mass–length scaling law was developed based on the assumption that muscle volume and body mass are linearly related, which was confirmed by the obtained volume and body mass data. Two musculoskeletal models were developed for each subject: one in which peak isometric muscle forces were estimated using the mass–length scaling law; and another in which these parameters were determined directly from the MR-derived muscle volumes. Musculoskeletal modeling and quantitative gait analysis were then used to calculate lower-limb muscle forces in normal walking. The patterns of muscle forces predicted by the model with scaled peak isometric force values were similar to those predicted by the MR-based model, implying that assessments of muscle function obtained from these two methods are practically equivalent. These results support the use of mass–length scaling in the development of subject-specific musculoskeletal models of children.

An open source lower limb model: Hip joint validation

Musculoskeletal lower limb models have been shown to be able to predict hip contact forces (HCFs) that are comparable to in vivo measurements obtained from instrumented prostheses. However, the muscle recruitment predicted by these models does not necessarily compare well to measured electromyographic (EMG) signals.In order to verify if it is possible to accurately estimate HCFs from muscle force patterns consistent with EMG measurements, a lower limb model based on a published anatomical dataset (Klein Horsman et al., 2007. Clinical Biomechanics. 22, 239–247) has been implemented in the open source software OpenSim. A cycle-to-cycle hip joint validation was conducted against HCFs recorded during gait and stair climbing trials of four arthroplasty patients (Bergmann et al., 2001. Journal of Biomechanics. 34, 859–871). Hip joint muscle tensions were estimated by minimizing a polynomial function of the muscle forces. The resulting muscle activation patterns obtained by assessing multiple powers of the objective function were compared against EMG profiles from the literature. Calculated HCFs denoted a tendency to monotonically increase their magnitude when raising the power of the objective function; the best estimation obtained from muscle forces consistent with experimental EMG profiles was found when a quadratic objective function was minimized (average overestimation at experimental peak frame: 10.1% for walking, 7.8% for stair climbing).The lower limb model can produce appropriate balanced sets of muscle forces and joint contact forces that can be used in a range of applications requiring accurate quantification of both. The developed model is available at the website https://simtk.org/home/low_limb_london.

Force pattern characterisation of Caenorhabditis elegans in motion

Caenorhabditis elegans is a worm that could be mutated to have different muscle arms, which may generate distinct force patterns when the worm moves. In this paper, an integrated system employing both a novel PDMS device and a visual feedback from the device is reported. The silicone elastomer-based PDMS device consists of arrays of pillars, which form open channels for the worm to move in and bend the pillars in contact. Enabled by a single vision sensor (CCD/CMOS) camera, the computer vision system is able to transform the forces generated by C. elegans, through detecting the deflection of the pillars with sub-pixel accuracy. The experimental results demonstrate that the current vision-based force sensing system is capable of performing robust force measurements at a full 30 Hz with a 1.52 μN resolution. The framework has the potential to significantly facilitate the study on the relationship between muscle arms and force patterns of C. elegans in motion, and thus gives a better understanding of muscle arms development and modelling.

Modeling and simulation of muscle forces of trans-tibial amputee to study effect of prosthetic alignment

The muscle forces tend to change when any musculoskeletal system is damaged. It is necessary to predict and explain the patterns of muscle forces in the stump of a left trans-tibial amputee during walking, and to study the effects of the prosthetic alignment. Musculoskeletal modeling and computer simulation were combined to calculate muscle forces in the trans-tibial lower limb during walking. The prosthesis was aligned to be in optimal position for the subject and then changed into +6 degrees and -6 degrees in the sagittal plane relatively. Kinematic data of the stump wearing a prosthesis and ground reaction forces were simultaneously recorded by a gait analysis system and a force platform, respectively. The data were input into a model of the lower trans-tibial extremity with three-dimensional geometry and the corresponding seven muscle forces were predicted by a static optimization. Muscles performed much more actively in stance than in swing phase. Most muscles appeared very active around both heel-strike and toe-off. Muscle forces predicted were similar to that in temporal distribution at all three alignment conditions but the major muscles such as gluteus maximus, hamstrings, vasti and rectus femoris generated remarkable greater forces in -6 degrees and +6 degrees alignments than the normal condition. The above results showed the muscle forces increasing at the mal-alignment. Because the incorrect alignment could break the relative position of the socket and foot, and that would generate the extra joint moments. As a result, muscle forces increased, and the long-duration fatigue occurs more easily. The finding suggests that the proper prosthetic alignment is very important for the stump muscles normal activities.

Are natural coordinates a useful tool in modeling planar biomechanical linkages?

The paper analyzes the use of natural coordinates in modeling and simulation of musculoskeletal models of the human body. A biomechanical model of the lower extremity of the human body was constructed for the analysis. It consists of three anatomical segments described by eight natural coordinates located at the joints. The developed model was applied to solve three classic dynamics problems of human motion: inverse dynamics, direct dynamics, and static optimization. The analysis covers the raising of a leg together with; the time characteristics of the resultant net torques at the basic joints of the leg (inverse dynamics), the time histories of natural coordinates and their velocities (direct dynamics) as well as the time-varying muscle force patterns (static optimization). In order to check the numerical efficiency of modeling in the natural coordinates’ environment the results were compared with the ones received through generalized coordinates. Some conclusions drawn from this comparison and final remarks referring to the biomechanical identification of the analyzed motor task were included in the paper.

Estimation of the muscle force distribution in ballistic motion based on a multibody methodology

This work presents a general three-dimensional multibody procedure for studying the human body motion with emphasis on the locomotion apparatus. The methodology includes a three-dimensional biomechanical model, data acquisition techniques and an inverse dynamics approach. The biomechanical model is based on a multibody formulation using natural coordinates and consists of 16 anatomical segments modeled by 33 rigid bodies for a total of 44 degrees-of-freedom. The action of the muscles is introduced in the equations of motion of the multibody model by means of driver actuators defined as kinematic constraints. By associating a Lagrange multiplier to each muscle actuator the muscle forces became coupled with the biomechanical model through the Jacobian matrix of the underlying multibody system. A Hill type muscle model is used to calculate individual muscle forces. The model for the muscle apparatus comprises 43 muscle groups for each leg, which use the full three-dimensional lines of action for these muscles in their geometric description. The problem of the redundancy of the forces on the musculoskeletal structure is solved by using inverse dynamics and static optimization methods. In the process of describing the methodology the benefits of modeling in natural coordinates are highlighted. The methodology developed is demonstrated through its application to a case of ballistic motion, represented by the take-off to an aerial trajectory in order to estimate the joint torques and the muscle force distribution in the supporting leg. The time characteristics of the resultant net torques at the basic joints of the supporting leg and the time-varying muscle force patterns are presented and discussed. The results obtained are explained in terms of their relevance to the activity under study.

A ‘cheap’ optimal control approach to estimate muscle forces in musculoskeletal systems

This paper shows a new method to estimate the muscle forces in musculoskeletal systems based on the inverse dynamics of a multi-body system associated optimal control. The redundant actuator problem is solved by minimizing a time-integral cost function, augmented with a torque-tracking error function, and muscle dynamics is considered through differential constraints. The method is compared to a previously implemented human posture control problem, solved using a Forward Dynamics Optimal Control approach and to classical static optimization, with two different objective functions. The new method provides very similar muscle force patterns when compared to the forward dynamics solution, but the computational cost is much smaller and the numerical robustness is increased. The results achieved suggest that this method is more accurate for the muscle force predictions when compared to static optimization, and can be used as a numerically ‘cheap’ alternative to the forward dynamics and optimal control in some applications.

A Proprioception Based Regulation Model to Estimate the Trunk Muscle Forces

Evaluation of loads acting on the spine requires the knowledge of the muscular forces acting on it, but muscles redundancy necessitates developing a muscle forces attribution strategy. Optimisation, EMG, or hybrid models allow evaluating muscle force patterns, yielding a unique muscular arrangement or/and requiring EMG data collection. This paper presents a regulation model of the trunk muscles based on a proprioception hypothesis, which searches to avoid the spinal joint overloading. The model is also compared to other existing models for evaluation. Compared to an optimisation model, the proposed alternative muscle pattern yielded a significant spine postero-anterior shear decrease. Compared to a model based on combination of optimisation criteria, present model better fits muscle activation observed using EMG (38% improvement). Such results suggest that the proposed model, based on regulation of all spinal components, may be more relevant from a physiologic point of view.