Comparison of an EMG-based and a stress-based method to predict shoulder muscle forces

The estimation of muscle forces in musculoskeletal shoulder models is still controversial. Two different methods are widely used to solve the indeterminacy of the system: electromyography (EMG)-based methods and stress-based methods. The goal of this work was to evaluate the influence of these two methods on the prediction of muscle forces, glenohumeral load and joint stability after total shoulder arthroplasty. An EMG-based and a stress-based method were implemented into the same musculoskeletal shoulder model. The model replicated the glenohumeral joint after total shoulder arthroplasty. It contained the scapula, the humerus, the joint prosthesis, the rotator cuff muscles supraspinatus, subscapularis and infraspinatus and the middle, anterior and posterior deltoid muscles. A movement of abduction was simulated in the plane of the scapula. The EMG-based method replicated muscular activity of experimentally measured EMG. The stress-based method minimised a cost function based on muscle stresses. We compared muscle forces, joint reaction force, articular contact pressure and translation of the humeral head. The stress-based method predicted a lower force of the rotator cuff muscles. This was partly counter-balanced by a higher force of the middle part of the deltoid muscle. As a consequence, the stress-based method predicted a lower joint load (16% reduced) and a higher superior–inferior translation of the humeral head (increased by 1.2 mm). The EMG-based method has the advantage of replicating the observed cocontraction of stabilising muscles of the rotator cuff. This method is, however, limited to available EMG measurements. The stress-based method has thus an advantage of flexibility, but may overestimate glenohumeral subluxation.     

Estimation of temporal gait parameters using Bayesian models on acceleration signals

The purpose of this study is to develop a system capable of performing calculation of temporal gait parameters using two low-cost wireless accelerometers and artificial intelligence-based techniques as part of a larger research project for conducting human gait analysis. Ten healthy subjects of different ages participated in this study and performed controlled walking tests. Two wireless accelerometers were placed on their ankles. Raw acceleration signals were processed in order to obtain gait patterns from characteristic peaks related to steps. A Bayesian model was implemented to classify the characteristic peaks into steps or nonsteps. The acceleration signals were segmented based on gait events, such as heel strike and toe-off, of actual steps. Temporal gait parameters, such as cadence, ambulation time, step time, gait cycle time, stance and swing phase time, simple and double support time, were estimated from segmented acceleration signals. Gait data-sets were divided into two groups of ages to test Bayesian models in order to classify the characteristic peaks. The mean error obtained from calculating the temporal gait parameters was 4.6%. Bayesian models are useful techniques that can be applied to classification of gait data of subjects at different ages with promising results     

A preliminary study of modification of gait in real-time using surface electromyography

Modification of abnormal gait was attempted in real-time using a surface electromyography-based protocol to teach recruitment of the anterior tibialis at the correct time in the gait cycle. Two children diagnosed with cerebral palsy were able to learn volitional control of the anterior tibialis as demonstrated by improved clearance of the toe on the swing phase of the gait and newly learned ability to recruit and relax the anterior tibialis. The children were able to walk with the new gait pattern and reproduce the old one at will. Implications for future research in this area are discussed.     

Effect of body weight support variation on muscle activities during robot assisted gait: a dynamic simulation study

Background and Objectives: While body weight support (BWS) intonation is vital during conventional gait training of neurologically challenged subjects, it is important to evaluate its effect during robot assisted gait training. In the present research we have studied the effect of BWS intonation on muscle activities during robotic gait training using dynamic simulations. Methods: Two dimensional (2-D) musculoskeletal model of human gait was developed conjointly with another 2-D model of a robotic orthosis capable of actuating hip, knee and ankle joints simultaneously. The musculoskeletal model consists of eight major muscle groups namely; soleus (SOL), gastrocnemius (GAS), tibialis anterior (TA), hamstrings (HAM), vasti (VAS), gluteus maximus (GLU), uniarticular hip flexors (iliopsoas, IP), and Rectus Femoris (RF). BWS was provided at levels of 0, 20, 40 and 60% during the simulations. In order to obtain a feasible set of muscle activities during subsequent gait cycles, an inverse dynamics algorithm along with a quadratic minimization algorithm was implemented. Results: The dynamic parameters of the robot assisted human gait such as joint angle trajectories, ground contact force (GCF), human limb joint torques and robot induced torques at different levels of BWS were derived. The patterns of muscle activities at variable BWS were derived and analysed. For most part of the gait cycle (GC) the muscle activation patterns are quite similar for all levels of BWS as is apparent from the mean of muscle activities for the complete GC. Conclusions: Effect of BWS variation during robot assisted gait on muscle activities was studied by developing dynamic simulation. It is expected that the proposed dynamic simulation approach will provide important inferences and information about the muscle function variations consequent upon a change in BWS during robot assisted gait. This information shall be quite important while investigating the influence of BWS intonation on neuromuscular parameters of interest during robotic gait training.     

Using a cost function based on kinematics and electromyographic data to quantify muscle forces

A reliable evaluation of muscle forces in the human body is highly desirable for several applications in both clinical and research contexts. Several models of muscle force distribution based on non-invasive measurements have been proposed since 1836 (Weber and Weber, 1836), amongst which Crowninshield’s model (Crowninshield and Brand, 1981), which maximizes a cost-function representing the muscle fiber endurance, is the most popular. It is worth noting that Crowninshield’s model is the most widely adopted notwithstanding its major limitations of physiological coherence. Forster et al. (2004) pointed out that “these (conventional) criteria however do not predict co-contraction adequately”. Besides, electromyographic (EMG)-driven models have been proposed to assess individual muscle forces, which have not been broadly adopted due to their complexity and the need for a calibration before each test. In this context, a cost function based on kinematic and electromyographic data could provide the advantage of being physiologically more coherent with muscle activations compared to conventional cost-functions based on kinematics solely, and easier to use than the EMG-driven models. The objective of this study is to propose the first cost-function based on kinematics and electromyographic data to quantify muscle forces. When applying this new cost-function on a database of upper limb motions data of 17 subjects, healthy or with cerebral palsy, the muscle force prediction of the proposed model was 17.74% more coherent with the EMG pattern than the prediction of Crowninshield’s model. And on average, these results were more consistent whether the subjects were healthy or with cerebral palsy. In conclusion, we propose this cost-function for the quantification of muscle forces.     

On the potential of lower limb muscles to accelerate the body's centre of mass during walking

Quantification of lower limb muscle function during gait or other common activities may be achieved using an induced acceleration analysis, which determines the contributions of individual muscles to the accelerations of the body's centre of mass. However, this analysis is reliant on a mathematical optimisation for the distribution of net joint moments among muscles. One approach that overcomes this limitation is the calculation of a muscle's potential to accelerate the centre of mass based on either a unit-force or maximum-activation assumption. Unit-force muscle potential accelerations are determined by calculating the accelerations induced by a 1 N muscle force, whereas maximum-activation muscle potential accelerations are determined by calculating the accelerations induced by a maximally activated muscle. The aim of this study was to describe the acceleration potentials of major lower limb muscles during normal walking obtained from these two techniques, and to evaluate the results relative to absolute (optimisation-based) muscle-induced accelerations. Dynamic simulations of walking were generated for 10 able-bodied children using musculoskeletal models, and potential- and absolute induced accelerations were calculated using a perturbation method. While the potential accelerations often correctly identified the major contributors to centre-of-mass acceleration, they were noticeably different in magnitude and timing from the absolute induced accelerations. Potential induced accelerations predicted by the maximum-activation technique, which accounts for the force-generating properties of muscle, were no more consistent with absolute induced accelerations than unit-force potential accelerations. The techniques described may assist treatment decisions through quantitative analyses of common gait abnormalities and/or clinical interventions.     

Sensitivity analysis of an energetic muscle model applied at whole body level in recumbent pedalling

Musculoskeletal models are used in order to describe and analyse the mechanics of human movement. In order to get a complete evaluation of the human movement, energetic muscle models were developed and were shown to be promising.

The aim of this work is to determine the sensitivity of muscle mechanical and energetic model estimates to changes in parameters during recumbent pedalling.

Inputs of the model were electromyography and joint angles, collected experimentally on one participant. The sensitivity analysis was performed on muscle-specific tension, physiological cross-sectional area, muscle maximal force, tendon rest length and percentage of fast-twitch fibres using an integrated sensitivity ratio. Soleus, gastrocnemius, vasti, gluteus and medial hamstrings were selected for the analyses.

The energetic model was found to be always less sensitive to parameter changes than the mechanical model. Tendon slack length was found to be the most critical parameter for both energetic and mechanical models even if the effect on the energetic output was smaller than on muscle force and joint moments.     

A dynamic optimization technique for predicting muscle forces in the swing phase of gait

The muscle force sharing problem was solved for the swing phase of gait using a dynamic optimization algorithm. For comparison purposes the problem was also solved using a typical static optimization algorithm. The objective function for the dynamic optimization algorithm was a combination of the tracking error and the metabolic energy consumption. The latter quantity was taken to be the sum of the total work done by the muscles and the enthalpy change during the contraction. The objective function for the static optimization problem was the sum of the cubes of the muscle stresses. To solve the problem using the static approach, the inverse dynamics problem was first solved in order to determine the resultant joint torques required to generate the given hip, knee and ankle trajectories. To this effect the angular velocities and accelerations were obtained by numerical differentiation using a low-pass digital filter. The dynamic optimization problem was solved using the Fletcher-Reeves conjugate gradient algorithm, and the static optimization problem was solved using the Gradient-restoration algorithm. The results show influence of internal muscle dynamics on muscle control histories vis a vis muscle forces. They also illustrate the strong sensitivity of the results to the differentiation procedure used in the static optimization approach.     

A new parameter for quantifying the variability of surface electromyographic signals during gait: The occurrence frequency

Natural variability of myoelectric activity during walking was recently analyzed considering hundreds of strides. This allowed assessing a parameter seldom considered in classic surface EMG (sEMG) studies: the occurrence frequency, defined as the frequency each muscle activation occurs with, quantified by the number of strides when a muscle is recruited with that specific activation modality. Aim of present study was to propose the occurrence frequency as a new parameter for assessing sEMG-signal variability during walking. Aim was addressed by processing sEMG signals acquired from Gastrocnemius Lateralis, Tibialis Anterior, Rectus Femoris and Biceps femoris in 40 healthy subjects in order to: (1) show that occurrence frequency is not correlated with ON/OFF instants (R_mean = 0.11 ± 0.07; P > 0.05) and total time of activation (R_mean = 0.15 ± 0.08; P > 0.05); (2) confirm the above results by two handy examples of application (analysis of gender and age) which highlighted that significant (P < 0.05) gender-related and age-related differences within population were detected in occurrence frequency, but not in temporal sEMG parameters. In conclusion, present study demonstrated that occurrence frequency is able to provide further information, besides those supplied by classical temporal sEMG parameters and thus it is suitable to complement them in the evaluation of variability of myoelectric activity during walking.     

Forward dynamic optimization of handle path and muscle activity for handle based isokinetic wheelchair propulsion: A simulation study

Push-rim wheelchair propulsion is biomechanically inefficient and physiologically stressful to the musculoskeletal structure of human body. This study focuses to obtain a new, optimized propulsion shape for wheelchair users, which is within the ergonomic ranges of joint motion, thus reducing the probability of injuries. To identify the propulsion movement, forward dynamic optimization was performed on a 3D human musculoskeletal model linked to a handle based propulsion mechanism, having shape and muscle excitations as optimization variables. The optimization resulted in a handle path shape with a circularity ratio of 0.95, and produced a net propulsion power of 34.7 watts for an isokinetic propulsion cycle at 50?rpm. Compared to push-rim propulsion, the compact design of the new propulsion mechanism along with the ergonomically optimized propulsion shape may help to reduce the risk of injuries and thus improve the quality of life for wheelchair users.     

Predicting muscle forces in gait from EMG signals and musculotendon kinematics

An EMG-driven muscle model for determining muscle force-time histories during gait is presented. The model, based on Hill's equation (1938), incorporates morphological data and accounts for changes in musculotendon length, velocity, and the level of muscle excitation for both concentric and eccentric contractions. Musculotendon kinematics were calculated using three-dimensional cinematography with a model of the musculoskeletal system. Muscle force-length-EMG relations were established from slow isokinetic calibrations. Walking muscle force-time histories were determined for two subjects. Joint moments calculated from the predicted muscle forces were compared with moments calculated using a linked segment, inverse dynamics approach. Moment curve correlations ranged from r = 0.72 to R = 0.97 and the root mean square (RMS) differences were from 10 to 20 Nm. Expressed as a relative RMS, the moment differences ranged from a low of 23% at the ankle to a high of 72% at the hip. No single reason for the differences between the two moment curves could be identified. Possible explanations discussed include the linear EMG-to-force assumption and how well the EMG-to-force calibration represented excitation for the whole muscle during gait, assumptions incorporated in the muscle modeling procedure, and errors inherent in validating joint moments predicted from the model to moments calculated using linked segment, inverse dynamics. The closeness with which the joint moment curves matched in the present study supports using the modeling approach proposed to determine muscle forces in gait.     

Effect of electrode characteristics on electromyographic activity of the masseter muscle

ObjectivesThe study investigated effects of electrode material, inter-electrode distance (IED), and conductive gel on electromyographic (EMG) activity recorded from the masseter muscle.Materials and methodsEMG was recorded unilaterally, as ten volunteers performed standardized oral tasks. Ag/AgCl and Ag coated with Au were the gel-based; Ag alloy coated with graphene, pure Ag coated with graphene and silver nanowire embedded electrodes were the gel-free materials tested. Ag/AgCl electrodes were tested at three different IEDs (i.e. 15 mm, 20 mm, 25 mm). An electrode relative performance index (ERPI) was defined and calculated for each of the standardized oral tasks that the volunteers performed. ERPI values obtained for the different oral tasks with different electrode materials and IEDs were compared using two-way repeated-measures ANOVA.ResultsERPI values were not significantly influenced by IED. However, for the electrode materials statistically significant differences were found in ERPI values for all oral tasks. Of the gel-free electrode materials tested, pure silver electrodes coated with graphene had the highest ERPI values followed by Ag alloy electrodes coated with graphene and silver nanowire embedded electrodes.ConclusionsWithin the limitations of the study, IED between 15 and 25 mm has a negligible effect on masseter muscle EMG. Graphene coated and silver nanowire embedded electrodes show promise as gel-free alternatives.     

Sensitivity of muscle and intervertebral disc force computations to variations in muscle attachment sites

Abstract

The current paper aims at assessing the sensitivity of muscle and intervertebral disc force computations against potential errors in modeling muscle attachment sites. We perturbed each attachment location in a complete and coherent musculoskeletal model of the human spine and quantified the changes in muscle and disc forces during standing upright, flexion, lateral bending, and axial rotation of the trunk. Although the majority of the muscles caused minor changes (less than 5%) in the disc forces, certain muscle groups, for example, quadratus lumborum, altered the shear and compressive forces as high as 353% and 17%, respectively. Furthermore, percent changes were higher in the shear forces than in the compressive forces. Our analyses identified certain muscles in the rib cage (intercostales interni and intercostales externi) and lumbar spine (quadratus lumborum and longissimus thoracis) as being more influential for computing muscle and disc forces. Furthermore, the disc forces at the L4/L5 joint were the most sensitive against muscle attachment sites, followed by T6/T7 and T12/L1 joints. Presented findings suggest that modeling muscle attachment sites based on solely anatomical illustrations might lead to erroneous evaluation of internal forces and promote using anatomical datasets where these locations were accurately measured. When developing a personalized model of the spine, certain care should also be paid especially for the muscles indicated in this work.     

The effect of perturbing body segment parameters on calculated joint moments and muscle forces during gait

This study examined the effect of body segment parameter (BSP) perturbations on joint moments calculated using an inverse dynamics procedure and muscle forces calculated using computed muscle control (CMC) during gait. BSP (i.e. segment mass, center of mass location (com) and inertia tensor) of the left thigh, shank and foot of a scaled musculoskeletal model were perturbed. These perturbations started from their nominal value and were adjusted to ±40% in steps of 10%, for both individual as well as combined perturbations in BSP. For all perturbations, an inverse dynamics procedure calculated the ankle, knee and hip moments based on an identical inverse kinematics solution. Furthermore, the effect of applying a residual reduction algorithm (RRA) was investigated. Muscle excitations and resulting muscle forces were calculated using CMC. The results show only a limited effect of an individual parameter perturbation on the calculated moments, where the largest effect is found when perturbing the shank com (MS_com,shank, the ratio of absolute difference in torque and relative parameter perturbation, is maximally −7.81 N m for hip flexion moment). The additional influence of perturbing two parameters simultaneously is small (MS_{mass+com,thigh} is maximally 15.2 N m for hip flexion moment). RRA made small changes to the model to increase the dynamic consistency of the simulation (after RRA MS_com,shank is maximally 5.01 N m). CMC results show large differences in muscle forces when BSP are perturbed. These result from the underlying forward integration of the dynamic equations.