Modulation of limb dynamics in the swing phase of locomotion

A method was presented for quantifying cat (Felis catus) hind limb dynamics during swing phase of locomotion using a two-link rigid body model of leg and paw, which highlighted the dynamic interactions between segments. Comprehensive determination was made of cat segment parameters necessary for dynamic analysis, and regression equations were formulated to predict the inertial parameters of any comparable cat. Modulations in muscle and non-muscle components of knee and ankle joint moments were examined at two treadmill speeds using three gaits: (a) pace-like walk and trot-like walk, at 1.0 ms-1, and (b) gallop, at 2.1 ms-1. Results showed that muscle and segment interactive moments significantly effected limb trajectories during swing. Some moment components were greater in galloping than in walking, but net joint maxima were not significantly different between speeds. Moment magnitudes typically were greater for pace-like walking than for trot-like walking at the same speed. Generally, across gaits, the net and muscle moments were in phase with the direction of distal joint motion, and these same moments were out of phase with proximal joint motion. Intersegmental dynamics were not modulated exclusively by speed of locomotion, but interactive moments were also influenced significantly by gait mode.     

An efficient probabilistic methodology for incorporating uncertainty in body segment parameters and anatomical landmarks in joint loadings estimated from inverse dynamics

Inverse dynamics is a standard approach for estimating joint loadings in the lower extremity from kinematic and ground reaction data for use in clinical and research gait studies. Variability in estimating body segment parameters and uncertainty in defining anatomical landmarks have the potential to impact predicted joint loading. This study demonstrates the application of efficient probabilistic methods to quantify the effect of uncertainty in these parameters and landmarks on joint loading in an inverse-dynamics model, and identifies the relative importance of the parameters and landmarks to the predicted joint loading. The inverse-dynamics analysis used a benchmark data set of lower-extremity kinematics and ground reaction data during the stance phase of gait to predict the three-dimensional intersegmental forces and moments. The probabilistic analysis predicted the 1-99 percentile ranges of intersegmental forces and moments at the hip, knee, and ankle. Variabilities, in forces and moments of up to 56% and 156% of the mean values were predicted based on coefficients of variation less than 0.20 for the body segment parameters and standard deviations of 2 mm for the anatomical landmarks. Sensitivity factors identified the important parameters for the specific joint and component directions. Anatomical landmarks affected moments to a larger extent than body segment parameters. Additionally, for forces, anatomical landmarks had a larger effect than body segment parameters, with the exception of segment masses, which were important to the proximal-distal joint forces. The probabilistic modeling approach predicted the range of possible joint loading, which has implications in gait studies, clinical assessments, and implant design evaluations.     

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.     

Whole body inverse dynamics over a complete gait cycle based only on measured kinematics

This paper presents a three-dimensional (3D) whole body multi-segment model for inverse dynamics analysis over a complete gait cycle, based only on measured kinematic data. The sequence of inverse dynamics calculations differs significantly from the conventional application of inverse dynamics using force plate data. A new validated "Smooth Transition Assumption" was used to solve the indeterminacy problem in the double support phase. Kinematic data is required for all major body segments and, hence, a whole body gait measurement protocol is presented. Finally, sensitivity analyses were conducted to evaluate the effects of digital filtering and body segment parameters on the accuracy of the prediction results. The model gave reasonably good estimates of sagittal plane ground forces and moment; however, the estimates in the other planes were less good, which we believe is largely due to their small magnitudes in comparison to the sagittal forces and moment. The errors observed are most likely caused by errors in the kinematic data resulting from skin movement artefact and by errors in the estimated body segment parameters. A digital filtering cut-off frequency of 4.5Hz was found to produce the best results. It was also shown that errors in the mass properties of body segments can play a crucial role, with changes in properties sometimes having a disproportionate effect on the calculated ground reactions. The implication of these results is that, even when force plate data is available, the estimated joint forces are likely to suffer from similar errors.     

Comparative analysis of methods for estimating arm segment parameters and joint torques from inverse dynamics

A common problem in the analyses of upper limb unfettered reaching movements is the estimation of joint torques using inverse dynamics. The inaccuracy in the estimation of joint torques can be caused by the inaccuracy in the acquisition of kinematic variables, body segment parameters (BSPs), and approximation in the biomechanical models. The effect of uncertainty in the estimation of body segment parameters can be especially important in the analysis of movements with high acceleration. A sensitivity analysis was performed to assess the relevance of different sources of inaccuracy in inverse dynamics analysis of a planar arm movement. Eight regression models and one water immersion method for the estimation of BSPs were used to quantify the influence of inertial models on the calculation of joint torques during numerical analysis of unfettered forward arm reaching movements. Thirteen subjects performed 72 forward planar reaches between two targets located on the horizontal plane and aligned with the median plane. Using a planar, double link model for the arm with a floating shoulder, we calculated the normalized joint torque peak and a normalized root mean square (rms) of torque at the shoulder and elbow joints. Statistical analyses quantified the influence of different BSP models on the kinetic variable variance for given uncertainty on the estimation of joint kinematics and biomechanical modeling errors. Our analysis revealed that the choice of BSP estimation method had a particular influence on the normalized rms of joint torques. Moreover, the normalization of kinetic variables to BSPs for a comparison among subjects showed that the interaction between the BSP estimation method and the subject specific somatotype and movement kinematics was a significant source of variance in the kinetic variables. The normalized joint torque peak and the normalized root mean square of joint torque represented valuable parameters to compare the effect of BSP estimation methods on the variance in the population of kinetic variables calculated across a group of subjects with different body types. We found that the variance of the arm segment parameter estimation had more influence on the calculated joint torques than the variance of the kinematics variables. This is due to the low moments of inertia of the upper limb, especially when compared with the leg. Therefore, the results of the inverse dynamics of arm movements are influenced by the choice of BSP estimation method to a greater extent than the results of gait analysis.     

Differences in lower limb transverse plane joint moments during gait when expressed in two alternative reference frames

When comparing previous studies that have measured the three-dimensional moments acting about the lower limb joints (either external moments or opposing internal joint moments) during able-bodied adult gait, significant variation is apparent in the profiles of the reported transverse plane moments. This variation cannot be explained on the basis of adopted convention (i.e. external versus internal joint moment) or inherent variability in gait strategies. The aim of the current study was to determine whether in fact the frame in which moments are expressed has a dominant effect upon transverse plane moments and thus provides a valid explanation for the observed inconsistency in the literature. Kinematic and ground reaction force data were acquired from nine able-bodied adult subjects walking at a self-selected speed. Three-dimensional hip, knee and ankle joint moments during gait were calculated using a standard inverse dynamics approach. In addition to calculating internal joint moments, the components of the external moment occurring in the transverse plane at each of the lower limb joints were calculated to determine their independent effects. All moments were expressed in both the laboratory frame (LF) as well as the anatomical frame (AF) of the distal segment. With the exception of the ankle rotation moment in the foot AF, lower limb transverse plane joint moments during gait were found to display characteristic profiles that were consistent across subjects. Furthermore, lower limb transverse plane joint moments during gait differed when expressed in the distal segment AF compared to the LF. At the hip, the two alternative reference frames produced near reciprocal joint moment profiles. The components of the external moment revealed that the external ground reaction force moment was primarily responsible for this result. Lower limb transverse plane joint moments during gait were therefore found to be highly sensitive to a change in reference frame. These findings indicate that the different transverse plane joint moment profiles during able-bodied adult gait reported in the literature are likely to be explained on this basis.     

Changes in segmental inertial properties with age

The purpose of this study was to examine how the limb segment inertial parameters vary across the decades from the 1920s to the 1970s. Sixty-six males participated in this study, ranging in age from 20 to 79 years. Pre-screening ensured that all subjects were healthy. The inertial properties of the segments were determined by modeling each segment as series of geometric solids. A multivariate analysis of variance (ANOVA) revealed statistically significant differences between decade age groups for the upper arm, forearm, shank, and thigh (p<0.01). Subsequent ANOVAs revealed statistically significant differences for all the inertial properties for the upper arm, the center of mass location for the forearm, and segment mass for the thigh. Linear regression lines were fit to the data so that each inertial parameter for each segment could be predicted by subject's age, with the slope of this regression line indicating the trend in the data. These trends were statistically significant for all forearm inertial parameters, thigh mass and longitudinal moment of inertia, and forearm center of mass location. The changes for the thigh, upper arm, and forearm were consistent with the changes, which would accompany a change in muscle mass with aging. Resultant joint moments were computed for a set of gait data using inertial properties reflective of the subjects from the age extremes in the study. The resulting differences in the knee and hip moments, young versus old, were all less than 4.5%.     

Influence of body segments' parameters estimation models on inverse dynamics solutions during gait

The purpose of the present study was to examine the influence of anthropometric data on joint kinetics during gait. We particularly focused on the sensitivity of inverse dynamics solutions to the use of models for body segment parameters (BSP) estimation. Six often used estimation models were selected to provide BSP values for the three segments of the lower limb. Kinematics and dynamics were sampled from seven subjects performing barefoot gait at three different speeds. Joint kinetics were estimated with the bottom-up method using BSP values derived from each estimation model as anthropometric inputs. The BSP estimates were highly sensitive to the model used with deviations ranging from at least 9.73% up to 60%. Maximal variations of peak values for the hip joint flexion/extension moment during the swing phase were 20.11%. Hence, our findings suggest that the influence of BSP cannot be neglected. Observed deviations are especially due to the effect of varying simultaneously the mass, moments of inertia and the center of mass location values, according to the underlying relationship of interdependency linking each component. Considering both the differences found in joint kinetics and the level of accuracy of BSP models, evidence is provided that using multiple regression BSP estimation functions derived from Zatsiorsky and Seluyanov should be recommended to assess joint kinetics.     

Kinematics of lower limbs during walking are emulated by springy walking model with a compliantly connected,off-centered curvy foot

The dynamics of the center of mass (CoM) during walking and running at various gait conditions are well described by the mechanics of a simple passive spring loaded inverted pendulum (SLIP). Due to its simplicity, however, the current form of the SLIP model is limited at providing any further information about multi-segmental lower limbs that generate oscillatory CoM behaviors and their corresponding ground reaction forces. Considering that the dynamics of the CoM are simply achieved by mass-spring mechanics, we wondered whether any of the multi-joint motions could be demonstrated by simple mechanics. In this study, we expand a SLIP model of human locomotion with an off-centered curvy foot connected to the leg by a springy segment that emulates the asymmetric kinematics and kinetics of the ankle joint. The passive dynamics of the proposed expansion of the SLIP model demonstrated the empirical data of ground reaction forces, center of mass trajectories, ankle joint kinematics and corresponding ankle joint torque at various gait speeds. From the mechanically simulated trajectories of the ankle joint and CoM, the motion of lower-limb segments, such as thigh and shank angles, could be estimated from inverse kinematics. The estimation of lower limb kinematics showed a qualitative match with empirical data of walking at various speeds. The representability of passive compliant mechanics for the kinetics of the CoM and ankle joint and lower limb joint kinematics implies that the coordination of multi-joint lower limbs during gait can be understood with a mechanical framework.     

A probabilistic method to estimate gait kinetics in the absence of ground reaction force measurements

Human joint torques during gait are usually computed using inverse dynamics. This method requires a skeletal model, kinematics and measured ground reaction forces and moments (GRFM). Measuring GRFM is however only possible in a controlled environment. This paper introduces a probabilistic method based on probabilistic principal component analysis to estimate the joint torques for healthy gait without measured GRFM. A gait dataset of 23 subjects was obtained containing kinematics, measured GRFM and joint torques from inverse dynamics in order to obtain a probabilistic model. This model was then used to estimate the joint torques of other subjects without measured GRFM. Only kinematics, a skeletal model and timing of gait events are needed. Estimation only takes 0.28 ms per time instant. Using cross-validation, the resulting root mean square estimation errors for the lower-limb joint torques are found to be approximately 0.1 Nm/kg, which is 6–18% of the range of the ground truth joint torques. Estimated joint torque and GRFM errors are up to two times smaller than model-based state-of-the-art methods. Model-free artificial neural networks can achieve lower errors than our method, but are less repeatable, do not contain uncertainty information on the estimates and are difficult to use in situations which are not in the learning set. In contrast, our method performs well in a new situation where the walking speed is higher than in the learning dataset. The method can for example be used to estimate the kinetics during overground walking without force plates, during treadmill walking without (separate) force plates and during ambulatory measurements.     

A general model for estimating lower extremity inertial properties of individuals with transtibial amputation

Lower extremity joint moment magnitudes during swing are dependent on the inertial properties of the prosthesis and residual limb of individuals with transtibial amputation (TTA). Often, intact limb inertial properties (INTACT) are used for prosthetic limb values in an inverse dynamics model even though these values overestimate the amputated limb’s inertial properties. The purpose of this study was to use subject-specific (SPECIFIC) measures of prosthesis inertial properties to generate a general model (GENERAL) for estimating TTA prosthesis inertial properties. Subject-specific mass, center of mass, and moment of inertia were determined for the shank and foot segments of the prosthesis (n = 11) using an oscillation technique and reaction board. The GENERAL model was derived from the means of the SPECIFIC model. Mass and segment lengths are required GENERAL model inputs. Comparisons of segment inertial properties and joint moments during walking were made using three inertial models (unique sample; n = 9): (1) SPECIFIC, (2) GENERAL, and (3) INTACT. Prosthetic shank inertial properties were significantly smaller with the SPECIFIC and GENERAL model than the INTACT model, but the SPECIFIC and GENERAL model did not statistically differ. Peak knee and hip joint moments during swing were significantly smaller for the SPECIFIC and GENERAL model compared with the INTACT model and were not significantly different between SPECIFIC and GENERAL models. When subject-specific measures are unavailable, using the GENERAL model produces a better estimate of prosthetic side inertial properties resulting in more accurate joint moment measurements for individuals with TTA than the INTACT model.     

The role of intersegmental dynamics during rapid limb oscillations

The interactive dynamic effects of muscular, inertial and gravitational moments on rapid, multi-segmented limb oscillations were studied. Using three-segment, rigid-body equations of motion, hip, knee and ankle intersegmental dynamics were calculated for the steady-state cycles of the paw-shake response in adult spinal cats. Hindlimb trajectories were filmed to obtain segmental kinematics, and myopotentials of flexors and extensors at each of the three joints were recorded synchronously with the ciné film. The segmental oscillations that emerged during the paw-shake response were a consequence of an interplay between active and passive musculotendinous forces, inertial forces, and gravity. During steady-state oscillations, the amplitudes of joint excursions, peak angular velocities, and peak angular accelerations increased monotonically and significantly in magnitude from the proximal joint (hip) to the most distal joint (ankle). In contrast to these kinematic relationships, the maximal values of net moments at the hip and knee were equal in magnitude, but of significantly lower magnitude than the large net moment at the ankle joint. At both the ankle and the knee, the flexor and extensor muscle moments were equal, but at the hip the magnitude of the peak flexor muscle moment was significantly greater than the extensor muscle moment. Muscle moments at the hip not only acted to counterbalance accelerations of the more distal segments, but also acted to maintain the postural orientation of the hindlimb. Large muscle moments at the knee functioned to counterbalance the large inertial moments generated by the large angular accelerations of the paw. At the ankle, the muscle moments dominated the generation of the paw accelerations. At the ankle and the knee, muscle moments controlled limb dynamics by slowing and reversing joint motions, and the active muscle forces contributing to ankle and knee moments were derived from lengthening of active musculotendinous units. In contrast to the more distal joints, the active muscles crossing the hip predominantly shortened as a result of the interplay among inertial forces and gravitational moments. The muscle function and kinetic data explain key features of the complex interactions that occur between central control mechanisms and multi-segmented, oscillating limb segments during the paw-shake response.     

Influence of the 3D inverse dynamic method on the joint forces and moments during gait

The joint forces and moments are commonly used in gait analysis. They can be computed by four different 3D inverse dynamic methods proposed in the literature, either based on vectors and Euler angles, wrenches and quaternions, homogeneous matrices, or generalized coordinates and forces. In order to analyze the influence of the inverse dynamic method, the joint forces and moments were computed during gait on nine healthy subjects. A ratio was computed between the relative dispersions (due to the method) and the absolute amplitudes of the gait curves. The influence of the inverse dynamic method was negligible at the ankle (2%) but major at the knee and the hip joints (40%). This influence seems to be due to the dynamic computation rather than the kinematic computation. Compared to the influence of the joint center location, the body segment inertial parameter estimation, and more, the influence of the inverse dynamic method is at least of equivalent importance. This point should be confirmed with other subjects, possibly pathologic, and other movements.     

Compensation for inertial and gravity effects in a moving force platform

Force plates for human movement analysis provide accurate measurements when mounted rigidly on an inertial reference frame. Large measurement errors occur, however, when the force plate is accelerated, or tilted relative to gravity. This prohibits the use of force plates in human perturbation studies with controlled surface movements, or in conditions where the foundation is moving or not sufficiently rigid. Here we present a linear model to predict the inertial and gravitational artifacts using accelerometer signals. The model is first calibrated with data collected from random movements of the unloaded system and then used to compensate for the errors in another trial. The method was tested experimentally on an instrumented force treadmill capable of dynamic mediolateral translation and sagittal pitch. The compensation was evaluated in five experimental conditions, including platform motions induced by actuators, by motor vibration, and by human ground reaction forces. In the test that included all sources of platform motion, the root-mean-square (RMS) errors were 39.0 N and 15.3 N m in force and moment, before compensation, and 1.6 N and 1.1 N m, after compensation. A sensitivity analysis was performed to determine the effect on estimating joint moments during human gait. Joint moment errors in hip, knee, and ankle were initially 53.80 N m, 32.69 N m, and 19.10 N m, and reduced to 1.67 N m, 1.37 N m, and 1.13 N m with our method. It was concluded that the compensation method can reduce the inertial and gravitational artifacts to an acceptable level for human gait analysis.     

Bottom-up estimation of joint moments during manual lifting using orientation sensors instead of position sensors

L5/S1, hip and knee moments during manual lifting tasks are, in a laboratory environment, frequently established by bottom-up inverse dynamics, using force plates to measure ground reaction forces (GRFs) and an optoelectronic system to measure segment positions and orientations. For field measurements, alternative measurement systems are being developed. One alternative is the use of small body-mounted inertial/magnetic sensors (IMSs) and instrumented force shoes to measure segment orientation and GRFs, respectively. However, because IMSs measure segment orientations only, the positions of segments relative to each other and relative to the GRFs have to be determined by linking them, assuming fixed segment lengths and zero joint translation. This will affect the estimated joint positions and joint moments. This study investigated the effect of using segment orientations only (orientation-based method) instead of using orientations and positions (reference method) on three-dimensional joint moments. To compare analysis methods (and not measurement methods), GRFs were measured with a force plate and segment positions and/or orientations were measured using optoelectronic marker clusters for both analysis methods. Eleven male subjects lifted a box from floor level using three lifting techniques: a stoop, a semi-squat and a squat technique. The difference between the two analysis methods remained small for the knee moments: <4%. For the hip and L5/S1 moments, the differences were more substantial: up to 8% for the stoop and semi-squat techniques and up to 14% for the squat technique. In conclusion, joint moments during lifting can be estimated with good accuracy at the knee joint and with reasonable accuracy at the hip and L5/S1 joints using segment orientation and GRF data only.     

The knee adduction moment measured with an instrumented force shoe in patients with knee osteoarthritis

The external knee adduction moment (KAdM) during gait is an important parameter in patients with knee osteoarthritis (OA). KAdM measurement is currently restricted to instruments only available in gait laboratories. However, ambulatory movement analysis technology, including instrumented force shoes (IFS) and inertial and magnetic measurement systems (IMMS), can measure kinetics and kinematics of human gait free of laboratory restrictions. The objective of this study was a quantitative validation of the accuracy of the KAdM in patients with knee OA, when estimated with an ambulatory-based method (AmbBM) versus a laboratory-based method (LabBM). AmbBM is employing the IFS and a linked-segment model, while LabBM is based on a force plate and optoelectronic marker system. Effects of ground reaction force (GRF), centre of pressure (CoP), and knee joint position measurement are evaluated separately. Twenty patients with knee OA were measured. The GRFs showed differences up to 0.22 N/kg, the CoPs showed differences up to 4 mm, and the medio-lateral and vertical knee position showed differences to 9 mm, between AmbBM and LabBM. The GRF caused an under-estimation in KAdM in early stance. However, this effect was counteracted by differences in CoP and joint position, resulting in a net 5% over-estimation. In midstance and late stance the accuracy of the KAdM was mainly limited by use of the linked-segment model for joint position estimation, resulting in an under-estimation (midstance 6% and late stance 22%). Further improvements are needed in the estimation of joint position from segment orientation.     

Knee moment outcomes using inverse dynamics and the cross product function in moderate knee osteoarthritis gait: A comparison study

Inverse dynamics are the cornerstone of biomechanical assessments to calculate knee moments during walking. In knee osteoarthritis, these outcomes have been used to understand knee pathomechanics, but the complexity of an inverse dynamic model may limit the uptake of joint moments in some clinical and research structures. The objective was to determine whether discrete features of the sagittal and frontal plane knee moments calculated using inverse dynamics compare to knee moments calculated using a cross product function. Knee moments from 74 people with moderate knee osteoarthritis were assessed after ambulating at a self-selected speed on an instrumented dual belt treadmill. Standardized procedures were used for surface marker placement, gait speed determination and data processing. Net external frontal and sagittal plane knee moments were calculated using inverse dynamics and the three-dimensional position of the knee joint center with respect to the center of pressure was crossed with the three-dimensional ground reaction forces in the cross product function. Correlations were high between outcomes of the moment calculations (r > 0.9) and for peak knee adduction moment, knee adduction moment impulse and difference between peak flexion and extension moments, the cross product function resulted in absolute values less than 10% of those calculated using inverse dynamics in this treadmill walking environment. This computational solution may allow the integration of knee moment calculations to understand knee osteoarthritis gait without data collection or computational complexity.     

Non-driving intersegmental knee moments in cycling computed using a model that includes three-dimensional kinematics of the shank/foot and the effect of simplifying assumptions

Assessing the importance of non-driving intersegmental knee moments (i.e. varus/valgus and internal/external axial moments) on over-use knee injuries in cycling requires the use of a three-dimensional (3-D) model to compute these loads. The objectives of this study were: (1) to develop a complete, 3-D model of the lower limb to calculate the 3-D knee loads during pedaling for a sample of the competitive cycling population, and (2) to examine the effects of simplifying assumptions on the calculations of the non-driving knee moments. The non-driving knee moments were computed using a complete 3-D model that allowed three rotational degrees of freedom at the knee joint, included the 3-D inertial loads of the shank/foot, and computed knee loads in a shank-fixed coordinate system. All input data, which included the 3-D segment kinematics and the six pedal load components, were collected from the right limb of 15 competitive cyclists while pedaling at 225 W and 90 rpm. On average, the peak varus and internal axial moments of 7.8 and 1.5 N m respectively occurred during the power stroke whereas the peak valgus and external axial moments of 8.1 and 2.5 N m respectively occurred during the recovery stroke. However, the non-driving knee moments were highly variable between subjects; the coefficients of variability in the peak values ranged from 38.7% to 72.6%. When it was assumed that the inertial loads of the shank/foot for motion out of the sagittal plane were zero, the root-mean-squared difference (RMSD) in the non-driving knee moments relative to those for the complete model was 12% of the peak varus/valgus moment and 25% of the peak axial moment. When it was also assumed that the knee joint was revolute with the flexion/extension axis perpendicular to the sagittal plane, the RMSD increased to 24% of the peak varus/valgus moment and 204% of the peak axial moment. Thus, the 3-D orientation of the shank segment has a major affect on the computation of the non-driving knee moments, while the inertial contributions to these loads for motions out of the sagittal plane are less important.     

An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo

This paper examined if an electromyography (EMG) driven musculoskeletal model of the human knee could be used to predict knee moments, calculated using inverse dynamics, across a varied range of dynamic contractile conditions. Muscle-tendon lengths and moment arms of 13 muscles crossing the knee joint were determined from joint kinematics using a three-dimensional anatomical model of the lower limb. Muscle activation was determined using a second-order discrete non-linear model using rectified and low-pass filtered EMG as input. A modified Hill-type muscle model was used to calculate individual muscle forces using activation and muscle tendon lengths as inputs. The model was calibrated to six individuals by altering a set of physiologically based parameters using mathematical optimisation to match the net flexion/extension (FE) muscle moment with those measured by inverse dynamics. The model was calibrated for each subject using 5 different tasks, including passive and active FE in an isokinetic dynamometer, running, and cutting manoeuvres recorded using three-dimensional motion analysis. Once calibrated, the model was used to predict the FE moments, estimated via inverse dynamics, from over 200 isokinetic dynamometer, running and sidestepping tasks. The inverse dynamics joint moments were predicted with an average R(2) of 0.91 and mean residual error of approximately 12 Nm. A re-calibration of only the EMG-to-activation parameters revealed FE moments prediction across weeks of similar accuracy. Changing the muscle model to one that is more physiologically correct produced better predictions. The modelling method presented represents a good way to estimate in vivo muscle forces during movement tasks.     

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.