Are patient-specific joint and inertial parameters necessary for accurate inverse dynamics analyses of gait?

Joint kinematic and kinetic responses to added mass on the lower extremities during running

AIM

We analyzed the biomechanical response (joint angles, moments, and powers) to running with added leg mass. These data may help guide the design of wearable locomotor assistive devices (i.e., exoskeletons), which are becoming more prevalent.

METHODS

15 participants (7 females, 8 males) completed treadmill running trials (3m•s-1) normally and with lead mass (300-1350 g) attached to the thigh, shank, or foot, bilaterally. We quantified the lower limb biomechanics combining motion capture and ground reaction force data using standard inverse dynamics analysis.

RESULTS

Only moderate kinematic changes occurred in response to the distal added limb mass. Maximum hip flexion and maximum knee flexion angles during swing phase increased by approximately 9% and 6% respectively for each 1 kg added to each foot. However, adding even small masses made dramatic changes to the joint moments and powers, mostly during the swing phase. For example, adding 1 kg to each foot increased maximum joint moments by as much as 40% (knee extension in late swing) and maximum joint power by as much as 50% (hip generation in late swing).

CONCLUSION

Leg joint kinematics were largely conserved in response to adding mass to the legs. Adding mass to the leg distally increased joint power mainly at the knee and hip joints during the swing phase, whereas adding mass proximally mainly affected the ankle joint mechanics during the stance phase. These changes have implications for shoe designs, people who run with added mass on their legs for sport/strength training and for the design of wearable devices.

Comparison of plugin and redundant marker sets to analyze gait kinematics between different populations

BACKGROUND

Gait model consists of a marker set and a segment pose estimation algorithm. Plugin marker set and inverse kinematic algorithm (IK.) are prevalent in gait analysis, especially musculoskeletal motion analysis. Adding extra markers for the plugin marker set could increase the robustness to marker misplacement, motion artifacts, and even markers occlusion. However, how the different marker sets affect the gait analysis's kinematic output is unclear. Therefore, this study aims to investigate the effect of marker sets on the kinematic output during level walking in different populations.

RESULTS

In all three planes, there are significant differences (P < 0.05) between marker sets in some kinematic variables at the hip, knee, and ankle. In different populations, the kinematic variables that show significant differences varied. When comparing the kinematic differences between populations using the two marker sets separately, the range of motion (ROM) of hip flexion was only found to be a significant difference using the redundant marker set, while the peak internal rotation at the knee was only found a significant difference using plugin marker set. In addition, the redundant marker set shows less intra-subject variation than the plugin marker set.

CONCLUSION

The findings in this study demonstrate the importance of marker set selection since it could change the result when comparing the kinematic differences between populations. Therefore, it is essential to increase the caution in explaining the result when using different marker sets. It is crucial to use the same marker set, and the redundant marker set might be a better choice for gait analysis.

Statistical Quantification of the Effects of Marker Misplacement and Soft-Tissue Artifact on Shoulder Kinematics and Kinetics

The assessment of shoulder kinematics and kinetics are commonly undertaken biomechanically and clinically by using rigid-body models and experimental skin-marker trajectories. However, the accuracy of these trajectories is plagued by inherent skin-based marker errors due to marker misplacements (offset) and soft-tissue artifacts (STA). This paper aimed to assess the individual contribution of each of these errors to kinematic and kinetic shoulder outcomes computed using a shoulder rigid-body model. Baseline experimental data of three shoulder planar motions in a young healthy adult were collected. The baseline marker trajectories were then perturbed by simulating typically observed population-based offset and/or STA using a probabilistic Monte-Carlo approach. The perturbed trajectories were then used together with a shoulder rigid-body model to compute shoulder angles and moments and study their accuracy and variability against baseline. Each type of error was studied individually, as well as in combination. On average, shoulder kinematics varied by 3%, 6% and 7% due to offset, STA or combined errors, respectively. Shoulder kinetics varied by 11%, 27% and 28% due to offset, STA or combined errors, respectively. In conclusion, to reduce shoulder kinematic and kinetic errors, one should prioritise reducing STA as they have the largest error contribution compared to marker misplacements.

Conclusion or Illusion: Quantifying Uncertainty in Inverse Analyses From Marker-Based Motion Capture due to Errors in Marker Registration and Model Scaling

Estimating kinematics from optical motion capture with skin-mounted markers, referred to as an inverse kinematic (IK) calculation, is the most common experimental technique in human motion analysis. Kinematics are often used to diagnose movement disorders and plan treatment strategies. In many such applications, small differences in joint angles can be clinically significant. Kinematics are also used to estimate joint powers, muscle forces, and other quantities of interest that cannot typically be measured directly. Thus, the accuracy and reproducibility of IK calculations are critical. In this work, we isolate and quantify the uncertainty in joint angles, moments, and powers due to two sources of error during IK analyses: errors in the placement of markers on the model (marker registration) and errors in the dimensions of the model’s body segments (model scaling). We demonstrate that IK solutions are best presented as a distribution of equally probable trajectories when these sources of modeling uncertainty are considered. Notably, a substantial amount of uncertainty exists in the computed kinematics and kinetics even if low marker tracking errors are achieved. For example, considering only 2 cm of marker registration uncertainty, peak ankle plantarflexion angle varied by 15.9°, peak ankle plantarflexion moment varied by 26.6 N⋅m, and peak ankle power at push off varied by 75.9 W during healthy gait. This uncertainty can directly impact the classification of patient movements and the evaluation of training or device effectiveness, such as calculations of push-off power. We provide scripts in OpenSim so that others can reproduce our results and quantify the effect of modeling uncertainty in their own studies.

A Conceptual Blueprint for Making Neuromusculoskeletal Models Clinically Useful

The ultimate goal of most neuromusculoskeletal modeling research is to improve the treatment of movement impairments. However, even though neuromusculoskeletal models have become more realistic anatomically, physiologically, and neurologically over the past 25 years, they have yet to make a positive impact on the design of clinical treatments for movement impairments. Such impairments are caused by common conditions such as stroke, osteoarthritis, Parkinson’s disease, spinal cord injury, cerebral palsy, limb amputation, and even cancer. The lack of clinical impact is somewhat surprising given that comparable computational technology has transformed the design of airplanes, automobiles, and other commercial products over the same time period. This paper provides the author’s personal perspective for how neuromusculoskeletal models can become clinically useful. First, the paper motivates the potential value of neuromusculoskeletal models for clinical treatment design. Next, it highlights five challenges to achieving clinical utility and provides suggestions for how to overcome them. After that, it describes clinical, technical, collaboration, and practical needs that must be addressed for neuromusculoskeletal models to fulfill their clinical potential, along with recommendations for meeting them. Finally, it discusses how more complex modeling and experimental methods could enhance neuromusculoskeletal model fidelity, personalization, and utilization. The author hopes that these ideas will provide a conceptual blueprint that will help the neuromusculoskeletal modeling research community work toward clinical utility.

Musculoskeletal Model Personalization Affects Metabolic Cost Estimates for Walking

Assessment of metabolic cost as a metric for human performance has expanded across various fields within the scientific, clinical, and engineering communities. As an alternative to measuring metabolic cost experimentally, musculoskeletal models incorporating metabolic cost models have been developed. However, to utilize these models for practical applications, the accuracy of their metabolic cost predictions requires improvement. Previous studies have reported the benefits of using personalized musculoskeletal models for various applications, yet no study has evaluated how model personalization affects metabolic cost estimation. This study investigated the effect of musculoskeletal model personalization on estimates of metabolic cost of transport (CoT) during post-stroke walking using three commonly used metabolic cost models. We analyzed walking data previously collected from two male stroke survivors with right-sided hemiparesis. The three metabolic cost models were implemented within three musculoskeletal modeling approaches involving different levels of personalization. The first approach used a scaled generic OpenSim model and found muscle activations via static optimization (SOGen). The second approach used a personalized electromyographic (EMG)-driven musculoskeletal model with personalized functional axes but found muscle activations via static optimization (SOCal). The third approach used the same personalized EMG-driven model but calculated muscle activations directly from EMG data (EMGCal). For each approach, the muscle activation estimates were used to calculate each subject’s CoT at different gait speeds using three metabolic cost models (Umberger et al., 2003; Bhargava et al., 2004; Umberger, 2010). The calculated CoT values were compared with published CoT data as a function of walking speed, step length asymmetry, stance time asymmetry, double support time asymmetry, and severity of motor impairment (i.e., Fugl-Meyer score). Overall, only SOCal and EMGCal with the Bhargava metabolic cost model were able to reproduce accurately published experimental trends between CoT and various clinical measures of walking asymmetry post-stroke. Tuning of the parameters in the different metabolic cost models could potentially resolve the observed CoT magnitude differences between model predictions and experimental measurements. Realistic CoT predictions may allow researchers to predict human performance, surgical outcomes, and rehabilitation outcomes reliably using computational simulations.

Automated creation and tuning of personalised muscle paths for OpenSim musculoskeletal models of the knee joint

Computational modelling is an invaluable tool for investigating features of human locomotion and motor control which cannot be measured except through invasive techniques. Recent research has focussed on creating personalised musculoskeletal models using population-based morphing or directly from medical imaging. Although progress has been made, robust definition of two critical model parameters remains challenging: (1) complete tibiofemoral (TF) and patellofemoral (PF) joint motions, and (2) muscle tendon unit (MTU) pathways and kinematics (i.e. lengths and moment arms). The aim of this study was to develop an automated framework, using population-based morphing approaches to create personalised musculoskeletal models, consisting of personalised bone geometries, TF and PF joint mechanisms, and MTU pathways and kinematics. Informed from medical imaging, personalised rigid body TF and PF joint mechanisms were created. Using atlas- and optimisation-based methods, personalised MTU pathways and kinematics were created with the aim of preventing MTU penetration into bones and achieving smooth MTU kinematics that follow patterns from existing literature. This framework was integrated into the Musculoskeletal Atlas Project Client software package to create and optimise models for 6 participants with incrementally increasing levels of personalisation with the aim of improving MTU kinematics and pathways. Three comparisons were made: (1) non-optimised (Model 1) and optimised models (Model 3) with generic joint mechanisms; (2) non-optimised (Model 2) and optimised models (Model 4) with personalised joint mechanisms; and (3) both optimised models (Model 3 and 4). Following optimisation, improvements were consistently shown in pattern similarity to cadaveric data in comparison (1) and (2). For comparison (3), a number of comparisons showed no significant difference between the two compared models. Importantly, optimisation did not produce statistically significantly worse results in any case.

In Silico-Enhanced Treatment and Rehabilitation Planning for Patients with Musculoskeletal Disorders: Can Musculoskeletal Modelling and Dynamic Simulations Really Impact Current Clinical Practice?

Over the past decades, the use of computational physics-based models representative of the musculoskeletal (MSK) system has become increasingly popular in many fields of clinically driven research, locomotor rehabilitation in particular. These models have been applied to various functional impairments given their ability to estimate parameters which cannot be readily measured in vivo but are of interest to clinicians. The use of MSK modelling and simulations allows analysis of relevant MSK biomarkers such as muscle and joint contact loading at a number of different stages in the clinical treatment pathway in order to benefit patient functional outcome. Applications of these methods include optimisation of rehabilitation programs, patient stratification, disease characterisation, surgical pre-planning, and assistive device and exoskeleton design and optimisation. This review provides an overview of current approaches, the components of standard MSK models, applications, limitations, and assumptions of these modelling and simulation methods, and finally proposes a future direction.

Single-leg forward hopping exposures adversely affect knee joint health among persons with unilateral lower limb loss: A predictive model

Single-leg hopping is an atypical, yet convenient, method of ambulation for individuals who have sustained unilateral lower limb-loss. Hopping is generally discouraged by therapists but many patients report hopping, and the potential deleterious effects of frequent hopping on knee joint health remains unclear. Mechanical fatigue due to repeated exposures to increased or abnormal loading on the intact limb is thought to be a primary contributor to the high prevalence of knee osteoarthritis among individuals with unilateral lower limb amputation. We aimed to compare knee joint mechanics between single-leg hopping and walking at self-selected paces among individuals with unilateral lower limb-loss, and estimated the associated probability of knee cartilage failure. Thirty-two males with traumatic unilateral lower limb-loss (22 transtibial, 10 transfemoral) hopped and walked at a self-selected pace along a 15-m walkway. Peak knee moments were input to a phenomenological model of cartilage fatigue to estimate the damage and long-term failure probability of the medial knee cartilage when hopping vs. walking. We estimate that each hop accumulates as much damage as at least 8 strides of walking (p < 0.001), and each meter of hopping accumulates as much damage as at least 12 m of walking (p < 0.001). The 30-year failure probability of the medial knee cartilage exceeded a "coin-flip" chance (50%) when performing more than 197 hops per day. Although a convenient mode of ambulation for persons with unilateral lower limb-loss, to mitigate risk for knee osteoarthritis it is advisable to minimize exposure to single-leg forward hopping.

Prediction of Individual Knee Kinematics From an MRI Representation of the Articular Surfaces

OBJECTIVE

The knowledge of individual joint motion may help to understand the articular physiology and to design better treatments and medical devices. Measurements of in-vivo individual motion are nowadays invasive/ionizing (fluoroscopy) or imprecise (skin markers). We propose a new approach to derive the individual knee natural motion from a three-dimensional representation of articular surfaces.

METHODS

We hypothesize that tissue adaptation shapes articular surfaces to optimize load distribution. Thus, the knee natural motion is obtained as the envelope of tibiofemoral positions and orientations that minimize peak contact pressure, i.e. that maximize joint congruence. We investigated four in-vitro and one in-vivo knees. Articular surfaces were reconstructed from a reference MRI. Natural motion was computed by congruence maximization and results were validated versus experimental data, acquired through bone implanted markers, in-vitro, and single-plane fluoroscopy, in-vivo.

RESULTS

In two cases, one of which in-vivo, maximum mean absolute error stays below 2.2° and 2.7 mm for rotations and translations, respectively. The remaining knees showed differences in joint internal rotation between the reference MRI and experimental motion at 0° flexion, possibly due to some laxity. The same difference is found in the model predictions, which, however, still replicate the individual knee motion.

CONCLUSION

The proposed approach allows the prediction of individual joint motion based on non-ionizing MRI data.

SIGNIFICANCE

This method may help to characterize healthy and, by comparison, pathological knee behavior. Moreover, it may provide an individual reference motion for the personalization of musculoskeletal models, opening the way to their clinical application.

Do Muscle Synergies Improve Optimization Prediction of Muscle Activations During Gait?

Determination of muscle forces during motion can help to understand motor control, assess pathological movement, diagnose neuromuscular disorders, or estimate joint loads. Difficulty of in vivo measurement made computational analysis become a common alternative in which, as several muscles serve each degree of freedom, the muscle redundancy problem must be solved. Unlike static optimization (SO), synergy optimization (SynO) couples muscle activations across all time frames, thereby altering estimated muscle co-contraction. This study explores whether the use of a muscle synergy structure within an SO framework improves prediction of muscle activations during walking. A motion/force/electromyography (EMG) gait analysis was performed on five healthy subjects. A musculoskeletal model of the right leg actuated by 43 Hill-type muscles was scaled to each subject and used to calculate joint moments, muscle–tendon kinematics, and moment arms. Muscle activations were then estimated using SynO with two to six synergies and traditional SO, and these estimates were compared with EMG measurements. Synergy optimization neither improved SO prediction of experimental activation patterns nor provided SO exact matching of joint moments. Finally, synergy analysis was performed on SO estimated activations, being found that the reconstructed activations produced poor matching of experimental activations and joint moments. As conclusion, it can be said that, although SynO did not improve prediction of muscle activations during gait, its reduced dimensional control space could be beneficial for applications such as functional electrical stimulation or motion control and prediction.

Minimising tibial fracture after unicompartmental knee replacement: A probabilistic finite element study

BACKGROUND

Periprosthetic tibial fracture after unicompartmental knee replacement is a challenging post-operative complication. Patients have an increased risk of mortality after fracture, the majority undergo further surgery, and the revision operations are less successful. Inappropriate surgical technique increases the risk of fracture, but it is unclear which technical aspects of the surgery are most problematic and no research has been performed on how surgical factors interact.

METHODS

Firstly, this study quantified the typical variance in surgical cuts made during unicompartmental knee replacement (determined from bones prepared by surgeons during an instructional course). Secondly, these measured distributions were used to create a probabilistic finite element model of the tibia after replacement. A thousand finite element models were created using the Monte Carlo method, representing 1000 virtual operations, and the risk of tibial fracture was assessed.

FINDINGS

Multivariate linear regression of the results showed that excessive resection depth and making the vertical cut too deep posteriorly increased the risk of fracture. These two parameters also had high variability in the prepared synthetic bones. The regression equation calculated the risk of fracture from three cut parameters (resection depth, vertical and horizonal posterior cuts) and fit the model results with 90% correlation.

INTERPRETATION

This study introduces for the first time the application of a probabilistic approach to predict the aetiology of fracture after unicompartmental knee replacement, providing unique insight into the relative importance of surgical saw cut variations. Targeted changes to operative technique can now be considered to seek to reduce the risk of periprosthetic fracture.

Accuracy and kinematics consistency of marker-based scaling approaches on a lower limb model: a comparative study with imagery data

Medical images are not typically included in protocol of motion laboratories. Thus, accurate scaling of musculoskeletal models from optoelectronic data are important for any biomechanical analysis. The aim of the current study was to identify a scaling method based on optoelectronic data, inspired from literature, which could offer the best trade-off between accurate geometrical parameters (segment lengths, orientation of joint axes, marker coordinates) and consistent inverse kinematics outputs (kinematic error, joint angles). The methods were applied on 26 subjects and assessed with medical imagery building EOS-based models, considered as a reference. The main contribution of this paper is to show that the marker-based scaling followed by an optimisation of orientation joint axes and markers local coordinates, gives the most consistent scaling and joint angles with EOS-based models. Thus, when a non-invasive mean with an optoelectronic system is considered, a marker-based scaling is preliminary needed to get accurate segment lengths and to optimise joint axes and marker local coordinates to reduce kinematic errors.AbbrevationsAJCAnkle joint centreCKEcumulative kinematic errorDoFdegree of freedomEBEOS-basedHBheight-basedHJChip joint centreKJCknee joint centreMBmarker-basedMSMmusculoskeletal modelsSPMstatistical parametric mappingSTAsoft tissue artifactEB_a.m∗EOS-based with optimised joint axes, and all model markers coordinatesMB_a.m∗marker-based with optimised joint axes, and all model markers coordinatesMB_l.a.mmarker-based with optimised segment lengths, joint axes, and selected model markers coordinatesASISanterior superior illiac spinePSISposterior superior illiac spine.

Multibody Kinematics Optimization for the Estimation of Upper and Lower Limb Human Joint Kinematics: A Systematized Methodological Review

Multibody kinematics optimization (MKO) aims to reduce soft tissue artefact (STA) and is a key step in musculoskeletal modeling. The objective of this review was to identify the numerical methods, their validation and performance for the estimation of the human joint kinematics using MKO. Seventy-four papers were extracted from a systematized search in five databases and cross-referencing. Model-derived kinematics were obtained using either constrained optimization or Kalman filtering to minimize the difference between measured (i.e., by skin markers, electromagnetic or inertial sensors) and model-derived positions and/or orientations. While hinge, universal, and spherical joints prevail, advanced models (e.g., parallel and four-bar mechanisms, elastic joint) have been introduced, mainly for the knee and shoulder joints. Models and methods were evaluated using: (i) simulated data based, however, on oversimplified STA and joint models; (ii) reconstruction residual errors, ranging from 4 mm to 40 mm; (iii) sensitivity analyses which highlighted the effect (up to 36 deg and 12 mm) of model geometrical parameters, joint models, and computational methods; (iv) comparison with other approaches (i.e., single body kinematics optimization and nonoptimized kinematics); (v) repeatability studies that showed low intra- and inter-observer variability; and (vi) validation against ground-truth bone kinematics (with errors between 1 deg and 22 deg for tibiofemoral rotations and between 3 deg and 10 deg for glenohumeral rotations). Moreover, MKO was applied to various movements (e.g., walking, running, arm elevation). Additional validations, especially for the upper limb, should be undertaken and we recommend a more systematic approach for the evaluation of MKO. In addition, further model development, scaling, and personalization methods are required to better estimate the secondary degrees-of-freedom (DoF).

A screening method to analyse the sensitivity of a lower limb multibody kinematic model

The study presents a screening method used to identify the influential parameters of a lower limb model including ligaments, at low numerical cost. Concerning multibody kinematics optimisation, the ligament parameters (isometric length) were found the most influential ones in a previous study. The screening method tested if they remain influential with minimised length variations. The most important parameters for tibiofemoral kinematics were the skin markers, segment lengths and joint parameters, including two ligaments. This was confirmed by a quantitative sensitivity analysis. The screening method has the potential to be used as a stand-alone procedure for a sensitivity analysis.

Uncertainty in Limb Configuration Makes Minimal Contribution to Errors Between Observed and Predicted Forces in a Musculoskeletal Model of the Rat Hindlimb

Subject-specific musculoskeletal models are increasingly used in biomedical applications to predict endpoint forces due to muscle activation, matching predicted forces to experimentally observed forces at a specific limb configuration. However, it is difficult to precisely measure the limb configuration at which these forces are observed. The consequent uncertainty in limb configuration might contribute to errors in model predictions. We therefore evaluated how uncertainties in limb configuration measurement contributed to errors in force prediction, using data from in vivo measurements in the rat hindlimb. We used a data-driven approach to estimate the uncertainty in estimated limb configuration and then used this configuration uncertainty to evaluate the consequent uncertainty in force predictions, using Monte Carlo simulations. We used subject-specific models of joint structures (i.e., centers and axes of rotation) in order to estimate limb configurations for each animal. The standard deviation of the distribution of predicted force directions resulting from configuration uncertainty was small, ranging between 0.27° and 3.05° across muscles. For most muscles, this standard deviation was considerably smaller than the error between observed and predicted forces (between 0.57° and 70.96°), suggesting that uncertainty in limb configuration could not explain inaccuracies in model predictions. Instead, our results suggest that inaccuracies in muscle model parameters, most likely in parameters specifying muscle moment arms, are the main source of prediction errors by musculoskeletal models in the rat hindlimb.

Development of a multibody model to assess efforts along the spine for the rehabilitation of adolescents with idiopathic scoliosis

INTRODUCTION

Gait analysis has often been recognized as helpful for the therapeutic follow-up of adolescents with idiopathic scoliosis (IS).

METHODS

A multibody model of the human body was developed to display the intervertebral efforts along the spine of each adolescent with IS, and highlight the efforts that significantly differ from typical age-matched healthy adolescents. The intervertebral efforts of one adolescent with IS and an age-matched adolescent during a complete gait cycle were computed and compared.

RESULTS

All intervertebral efforts are larger in the adolescent with IS compared to the healthy adolescent, except for the vertical torque. The average medio-lateral torque and force for the participant with IS are respectively 200% and 114% higher.

CONCLUSION

This study revealed that the pathological efforts are not concentrated around critical points but distributed along the spine. Thus, higher average efforts along the spine in adolescent with IS may influence the spine deformity due to mechanical modulations according to the Hueter-Volkmann Law. The potential of this model is promising for the therapeutic follow-up of adolescents with IS because it provides real-time efforts along the spine, as well as the corresponding information about the asymmetrical behavior of the spine during gait.

Influence of biomechanical models on joint kinematics and kinetics in baseball pitching

In baseball pitching, biomechanical parameters have been linked to ball velocity and potential injury risk. However, although the features of a biomechanical model have a significant influence on the kinematics and kinetics of a motion, this influence have not been assessed for pitching. The aim of this study was to evaluate the choice of the trunk and shoulder features, by comparing two models using the same input. The models differed in thoraco-humeral joint definition (moving or fixed with the thorax), joint centre estimation, values of the inertial parameters and computational framework. One professional pitcher participated in the study. We found that the different features of the biomechanical models have a substantial influence on the kinematics and kinetics of the pitchers. With a fixed thoraco-humeral joint the peak average thorax angular velocity was delayed and underestimated by 17% and the shoulder internal rotation velocity was overestimated by 7%. The use of a thoraco-humeral joint fixed to the thorax will lead to an overestimation of the rotational power at the shoulder and will neglect the power produced by the forward and upward translation of the shoulder girdle. These findings have direct implications for the interpretation of shoulder muscle contributions to the pitch.

Sensitivity analysis of the estimated muscle forces during gait with respect to the musculoskeletal model parameters and dynamic simulation techniques

The purpose of the current study was to investigate the robustness of dynamic simulation results in the presence of uncertainties resulting from application of a scaled-generic musculoskeletal model instead of a subject-specific model as well as the effect of the choice of simulation method on the obtained muscle forces. The performed sensitivity analysis consisted of the following multibody parameter modifications: maximum isometric muscle forces, number of muscles, the hip joint centre location, segment masses as well as different dynamic simulation methods, namely static optimization with three different criteria and a computed muscle control algorithm (hybrid approach combining forward and inverse dynamics). Twenty-four different models and fifty-five resultant dynamic simulation data sets were analysed. The effects of model perturbation on the magnitude and profile of muscle forces were compared. It has been shown that estimated muscle forces are very sensitive to model parameters. The greatest impact was observed in the case of the force magnitude of the muscles generating high forces during gait (regardless of the modification introduced). However, the force profiles of those muscles were preserved. Relatively large differences in muscle forces were observed for different simulation techniques, which included both magnitude and profile of muscle forces. Personalization of model parameters would affect the resultant muscle forces and seems to be necessary to improve general accuracy of the estimated parameters. However, personalization alone will not ensure high accuracy due to the still unresolved muscle force sharing problem.

Force Closure Mechanism Modeling for Musculoskeletal Multibody Simulation

OBJECTIVE

Neuro-musculoskeletal multibody simulation (NMBS) seeks to optimize decision-making for patients with neuro-musculoskeletal disorders. In clinical practice, however, the inter-subject variability and the inaccessibility for experimental testing impede the reliable model identification. These limitations motivate the novel modeling approach termed as force closure mechanism modeling (FCM²).

METHODS

FCM ² expresses the dynamics between mutually articulating joint partners with respect to instantaneous screw axes (ISA) automatically reconstructed from their relative velocity state. Thereby, FCM² reduces arbitrary open-chain multibody topologies to force closure n-link pendulums. Within a computational validation study on the human knee joint with implemented contact surfaces, we examine FCM² as an underlying inverse dynamic model for computed muscle control. We evaluate predicted tibiofemoral joint quantities, i.e., kinematics and contact forces along with muscle moment arms, during muscle-induced knee motion against the classic hinge joint model and experimental studies.

RESULTS

Our NMBS study provided the proof-of-principle of the novel modeling approach. FCM² freed us from assuming a certain joint formulation while correctly predicting the joint dynamics in agreement with the established methods. Although experimental results were closely predicted, owing to noise in the ISA estimation, muscle moment arms were overestimated (R_ISA = 0.84 < R_HINGE = 0.97, RMSE_ISA = 13.18 mm > RMSE_HINGE = 6.54 mm), identifying the robust ISA estimation as key to FCM².

CONCLUSION

FCM² automatically derives the equations of motion in closed form. Moreover, it captures subject-specific joint function and, thereby, minimizes modeling and parameterization efforts.

SIGNIFICANCE

Model derivation becomes driven by quantitative data available in clinical settings so that FCM² yields a promising framework toward subject-specific NMBS.

Methodological factors affecting joint moments estimation in clinical gait analysis: a systematic review

Quantitative gait analysis can provide a description of joint kinematics and dynamics, and it is recognized as a clinically useful tool for functional assessment, diagnosis and intervention planning. Clinically interpretable parameters are estimated from quantitative measures (i.e. ground reaction forces, skin marker trajectories, etc.) through biomechanical modelling. In particular, the estimation of joint moments during motion is grounded on several modelling assumptions: (1) body segmental and joint kinematics is derived from the trajectories of markers and by modelling the human body as a kinematic chain; (2) joint resultant (net) loads are, usually, derived from force plate measurements through a model of segmental dynamics. Therefore, both measurement errors and modelling assumptions can affect the results, to an extent that also depends on the characteristics of the motor task analysed (i.e. gait speed). Errors affecting the trajectories of joint centres, the orientation of joint functional axes, the joint angular velocities, the accuracy of inertial parameters and force measurements (concurring to the definition of the dynamic model), can weigh differently in the estimation of clinically interpretable joint moments. Numerous studies addressed all these methodological aspects separately, but a critical analysis of how these aspects may affect the clinical interpretation of joint dynamics is still missing. This article aims at filling this gap through a systematic review of the literature, conducted on Web of Science, Scopus and PubMed. The final objective is hence to provide clear take-home messages to guide laboratories in the estimation of joint moments for the clinical practice.

Lower extremity EMG-driven modeling of walking with automated adjustment of musculoskeletal geometry

Neuromusculoskeletal disorders affecting walking ability are often difficult to manage, in part due to limited understanding of how a patient’s lower extremity muscle excitations contribute to the patient’s lower extremity joint moments. To assist in the study of these disorders, researchers have developed electromyography (EMG) driven neuromusculoskeletal models utilizing scaled generic musculoskeletal geometry. While these models can predict individual muscle contributions to lower extremity joint moments during walking, the accuracy of the predictions can be hindered by errors in the scaled geometry. This study presents a novel EMG-driven modeling method that automatically adjusts surrogate representations of the patient’s musculoskeletal geometry to improve prediction of lower extremity joint moments during walking. In addition to commonly adjusted neuromusculoskeletal model parameters, the proposed method adjusts model parameters defining muscle-tendon lengths, velocities, and moment arms. We evaluated our EMG-driven modeling method using data collected from a high-functioning hemiparetic subject walking on an instrumented treadmill at speeds ranging from 0.4 to 0.8 m/s. EMG-driven model parameter values were calibrated to match inverse dynamic moments for five degrees of freedom in each leg while keeping musculoskeletal geometry close to that of an initial scaled musculoskeletal model. We found that our EMG-driven modeling method incorporating automated adjustment of musculoskeletal geometry predicted net joint moments during walking more accurately than did the same method without geometric adjustments. Geometric adjustments improved moment prediction errors by 25% on average and up to 52%, with the largest improvements occurring at the hip. Predicted adjustments to musculoskeletal geometry were comparable to errors reported in the literature between scaled generic geometric models and measurements made from imaging data. Our results demonstrate that with appropriate experimental data, joint moment predictions for walking generated by an EMG-driven model can be improved significantly when automated adjustment of musculoskeletal geometry is included in the model calibration process.

Cervical Spine Injuries: A Whole-Body Musculoskeletal Model for the Analysis of Spinal Loading

Cervical spine trauma from sport or traffic collisions can have devastating consequences for individuals and a high societal cost. The precise mechanisms of such injuries are still unknown as investigation is hampered by the difficulty in experimentally replicating the conditions under which these injuries occur. We harness the benefits of computer simulation to report on the creation and validation of i) a generic musculoskeletal model (MASI) for the analyses of cervical spine loading in healthy subjects, and ii) a population-specific version of the model (Rugby Model), for investigating cervical spine injury mechanisms during rugby activities. The musculoskeletal models were created in OpenSim, and validated against in vivo data of a healthy subject and a rugby player performing neck and upper limb movements. The novel aspects of the Rugby Model comprise i) population-specific inertial properties and muscle parameters representing rugby forward players, and ii) a custom scapula-clavicular joint that allows the application of multiple external loads. We confirm the utility of the developed generic and population-specific models via verification steps and validation of kinematics, joint moments and neuromuscular activations during rugby scrummaging and neck functional movements, which achieve results comparable with in vivo and in vitro data. The Rugby Model was validated and used for the first time to provide insight into anatomical loading and cervical spine injury mechanisms related to rugby, whilst the MASI introduces a new computational tool to allow investigation of spinal injuries arising from other sporting activities, transport, and ergonomic applications. The models used in this study are freely available at simtk.org and allow to integrate in silico analyses with experimental approaches in injury prevention.

Full-Body Musculoskeletal Model for Muscle-Driven Simulation of Human Gait

OBJECTIVE

Musculoskeletal models provide a non-invasive means to study human movement and predict the effects of interventions on gait. Our goal was to create an open-source 3-D musculoskeletal model with high-fidelity representations of the lower limb musculature of healthy young individuals that can be used to generate accurate simulations of gait.

METHODS

Our model includes bony geometry for the full body, 37 degrees of freedom to define joint kinematics, Hill-type models of 80 muscle-tendon units actuating the lower limbs, and 17 ideal torque actuators driving the upper body. The model's musculotendon parameters are derived from previous anatomical measurements of 21 cadaver specimens and magnetic resonance images of 24 young healthy subjects. We tested the model by evaluating its computational time and accuracy of simulations of healthy walking and running.

RESULTS

Generating muscle-driven simulations of normal walking and running took approximately 10 minutes on a typical desktop computer. The differences between our muscle-generated and inverse dynamics joint moments were within 3% (RMSE) of the peak inverse dynamics joint moments in both walking and running, and our simulated muscle activity showed qualitative agreement with salient features from experimental electromyography data.

CONCLUSION

These results suggest that our model is suitable for generating muscle-driven simulations of healthy gait. We encourage other researchers to further validate and apply the model to study other motions of the lower extremity.

SIGNIFICANCE

The model is implemented in the open-source software platform OpenSim. The model and data used to create and test the simulations are freely available at https://simtk.org/home/full_body/, allowing others to reproduce these results and create their own simulations.

Residual Elimination Algorithm Enhancements to Improve Foot Motion Tracking During Forward Dynamic Simulations of Gait

Patient-specific gait optimizations capable of predicting post-treatment changes in joint motions and loads could improve treatment design for gait-related disorders. To maximize potential clinical utility, such optimizations should utilize full-body three-dimensional patient-specific musculoskeletal models, generate dynamically consistent gait motions that reproduce pretreatment marker measurements closely, and achieve accurate foot motion tracking to permit deformable foot-ground contact modeling. This study enhances an existing residual elimination algorithm (REA) Remy, C. D., and Thelen, D. G., 2009, “Optimal Estimation of Dynamically Consistent Kinematics and Kinetics for Forward Dynamic Simulation of Gait,” ASME J. Biomech. Eng., 131(3), p. 031005) to achieve all three requirements within a single gait optimization framework. We investigated four primary enhancements to the original REA: (1) manual modification of tracked marker weights, (2) automatic modification of tracked joint acceleration curves, (3) automatic modification of algorithm feedback gains, and (4) automatic calibration of model joint and inertial parameter values. We evaluated the enhanced REA using a full-body three-dimensional dynamic skeletal model and movement data collected from a subject who performed four distinct gait patterns: walking, marching, running, and bounding. When all four enhancements were implemented together, the enhanced REA achieved dynamic consistency with lower marker tracking errors for all segments, especially the feet (mean root-mean-square (RMS) errors of 3.1 versus 18.4 mm), compared to the original REA. When the enhancements were implemented separately and in combinations, the most important one was automatic modification of tracked joint acceleration curves, while the least important enhancement was automatic modification of algorithm feedback gains. The enhanced REA provides a framework for future gait optimization studies that seek to predict subject-specific post-treatment gait patterns involving large changes in foot-ground contact patterns made possible through deformable foot-ground contact models.

Probabilistic sensitivity analysis of in-vehicle reach tasks for digital human models considering anthropometric measurement uncertainty

For design using digital human models, human anthropometry data are required as input and are extracted from measurements. There is inherent error associated with these measurements which impacts the output of the simulation. Current techniques in digital human modeling applications primarily employ deterministic methods which are not well suited for handling variability in anthropometric measurement. An alternative to deterministic methods is probabilistic/sensitivity analysis. This study presents a probabilistic sensitivity approach to gain insights into how uncertainty in anthropometric measurements can affect the results of a digital human model with the specific application of vehicle-related reach tasks. Sensitivity levels are found to determine the importance of variability in each joint angle and link length to the final reach. A55-degree of freedom (DOF) digital human model is introduced to demonstrate the sensitivity approach for reach tasks. Seven right-hand reach target points and two left-hand reach target points (creating a total of 14 reach tasks) within a vehicle are used to compare the sensitivities in the joint angles and link lengths resulting from measurement uncertainty. The results show that the importance of each joint angle or link length is dependent on the characteristics of the reach task and sensitivities for joint angles, and link lengths are different for each reach task.

Is my model good enough? Best practices for verification and validation of musculoskeletal models and simulations of movement

Computational modeling and simulation of neuromusculoskeletal (NMS) systems enables researchers and clinicians to study the complex dynamics underlying human and animal movement. NMS models use equations derived from physical laws and biology to help solve challenging real-world problems, from designing prosthetics that maximize running speed to developing exoskeletal devices that enable walking after a stroke. NMS modeling and simulation has proliferated in the biomechanics research community over the past 25 years, but the lack of verification and validation standards remains a major barrier to wider adoption and impact. The goal of this paper is to establish practical guidelines for verification and validation of NMS models and simulations that researchers, clinicians, reviewers, and others can adopt to evaluate the accuracy and credibility of modeling studies. In particular, we review a general process for verification and validation applied to NMS models and simulations, including careful formulation of a research question and methods, traditional verification and validation steps, and documentation and sharing of results for use and testing by other researchers. Modeling the NMS system and simulating its motion involves methods to represent neural control, musculoskeletal geometry, muscle-tendon dynamics, contact forces, and multibody dynamics. For each of these components, we review modeling choices and software verification guidelines; discuss variability, errors, uncertainty, and sensitivity relationships; and provide recommendations for verification and validation by comparing experimental data and testing robustness. We present a series of case studies to illustrate key principles. In closing, we discuss challenges the community must overcome to ensure that modeling and simulation are successfully used to solve the broad spectrum of problems that limit human mobility.

A probabilistic approach to quantify the impact of uncertainty propagation in musculoskeletal simulations

Uncertainty that arises from measurement error and parameter estimation can significantly affect the interpretation of musculoskeletal simulations; however, these effects are rarely addressed. The objective of this study was to develop an open-source probabilistic musculoskeletal modeling framework to assess how measurement error and parameter uncertainty propagate through a gait simulation. A baseline gait simulation was performed for a male subject using OpenSim for three stages: inverse kinematics, inverse dynamics, and muscle force prediction. A series of Monte Carlo simulations were performed that considered intrarater variability in marker placement, movement artifacts in each phase of gait, variability in body segment parameters, and variability in muscle parameters calculated from cadaveric investigations. Propagation of uncertainty was performed by also using the output distributions from one stage as input distributions to subsequent stages. Confidence bounds (5-95%) and sensitivity of outputs to model input parameters were calculated throughout the gait cycle. The combined impact of uncertainty resulted in mean bounds that ranged from 2.7° to 6.4° in joint kinematics, 2.7 to 8.1 N m in joint moments, and 35.8 to 130.8 N in muscle forces. The impact of movement artifact was 1.8 times larger than any other propagated source. Sensitivity to specific body segment parameters and muscle parameters were linked to where in the gait cycle they were calculated. We anticipate that through the increased use of probabilistic tools, researchers will better understand the strengths and limitations of their musculoskeletal simulations and more effectively use simulations to evaluate hypotheses and inform clinical decisions.

The effect of variability in body segment parameters on joint moment using Monte Carlo simulations

This study used Monte Carlo methods to simulate the effects of variability and uncertainty in inertial body segment parameters (BSPs) on joint torques calculated using inverse dynamics. The average and standard deviation values of BSPs from previously published studies were used as inputs into the Monte Carlo simulation. Data from five groups were evaluated: cadaveric subjects; living subjects (Caucasian only); female living subjects (Caucasian only); male living subjects (Caucasian only); and living subjects (non-Caucasian). The differences in BSPs observed between the different groups were statistically significant; however, using BSP variability data from these groups made little difference to the calculated joint torques. This suggests that for slow and repeatable movement such as walking, BSPs have little effect on joint moments, except for the swing phase. Even then, the magnitude of difference in the swing phase due to variability in BSPs is not much greater than the inter-trial variability. As expected, distal BSPs have little effect on proximal joint moment.

3D JOINT MOMENTS IN TRANSFEMORAL AND TRANSTIBIAL AMPUTEES: WHEN IS THE "GROUND REACTION VECTOR TECHNIQUE" AN ALTERNATIVE TO INVERSE DYNAMICS?

The aim of the present study was to identify the phases of gait and the joints where the "ground reaction vector technique" (GRVT) can represent an acceptable alternative to the use of inverse dynamics (ID), when considering subjects with a lower-limb amputation. First, an analytical investigation of the ID of the three joints of the lower limb is given, distinguishing the gravitational, the inertial and the ground reaction contributions. The first two contributions require inertial parameters estimation; for this purpose, literature anthropometric data are typically used, both for the unimpaired and prosthetic limb, as accurate specific inertial parameters for the prosthetic limb are difficult to obtain from companies or require time consuming estimation. This assumption potentially leads to errors in the three-dimensional (3D) joint moment estimation. Second, the results of two case studies, a trans-femoral amputee with two different prostheses and a trans-tibial amputee, showed that the GRVT can explain the most part of the net joint moment for the ankle and the knee in the whole stance phase, and for the hip in the first part of the stance, leading to a similar clinical evaluation without any assumptions on inertial parameters.

Optimizing whole-body kinematics to minimize valgus knee loading during sidestepping: implications for ACL injury risk

The kinematic mechanisms associated with elevated externally applied valgus knee moments during non-contact sidestepping and subsequent anterior cruciate ligament (ACL) injury risk are not well understood. To address this issue, the residual reduction algorithm (RRA) in OpenSim was used to create nine subject-specific, full-body (37 degrees of freedom) torque-driven simulations of athletic males performing unplanned sidestep (UnSS) sport tasks. The RRA was used again to produce an optimized kinematic solution with reduced peak valgus knee torques during the weight acceptance phase of stance. Pre-to-post kinematic optimization, mean peak valgus knee moments were significantly reduced by 44.2 Nm (p=0.045). Nine of a possible 37 upper and lower body kinematic changes in all three planes of motion were consistently used during the RRA to decrease peak valgus knee moments. The generalized kinematic strategy used by all nine simulations to reduce peak valgus knee moments and subsequent ACL injury risk during UnSS was to redirect the whole-body center of mass medially, towards the desired direction of travel.

The development of lower limb musculoskeletal models with clinical relevance is dependent upon the fidelity of the mathematical description of the lower limb. Part 2: patient-specific geometry

Musculoskeletal models have the potential to evolve into sensitive clinical tools that provide relevant therapeutic guidance. A key impediment to this is the lack of understanding as to the function of such models. In order to improve this it is useful to recognise that musculoskeletal modelling is the mathematical description of musculoskeletal movement – a process that involves the construction and solution of equations of motion. These equations are derived from standard mechanical considerations and the mathematical representation of anatomy. The fidelity of musculoskeletal models is highly dependent on the assumption that such representations also describe the function of the musculoskeletal geometry. In addition, it is important to understand the sensitivity of such representations to patient-specific variations in anatomy. The exploration of these twin considerations will be fundamental to the creation of musculoskeletal modelling tools with clinical relevance and a systematic enquiry of these key parameters is recommended.

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.

A review of probabilistic analysis in orthopaedic biomechanics

Probabilistic analysis methods are being increasingly applied in the orthopaedics and biomechanics literature to account for uncertainty and variability in subject geometries, properties of various structures, kinematics and joint loading, as well as uncertainty in implant alignment. As a complement to experiments, finite element modelling, and statistical analysis, probabilistic analysis provides a method of characterizing the potential impact of variability in parameters on performance. This paper presents an overview of probabilistic analysis and a review of biomechanics literature utilizing probabilistic methods in structural reliability, kinematics, joint mechanics, musculoskeletal modelling, and patient-specific representations. The aim of this review paper is to demonstrate the wide range of applications of probabilistic methods and to aid researchers and clinicians in better understanding probabilistic analyses.

The effect of knee model on estimates of muscle and joint forces in recumbent pedaling

The usefulness of forward dynamic simulations to studies of human motion is well known. Although the musculoskeletal models used in these studies are generic, the modeling of specific components, such as the knee joint, may vary. Our two objectives were (1) to investigate the effects of three commonly used knee models on forward dynamic simulation results, and (2) to study the sensitivity of simulation results to variations in kinematics for the most commonly used knee model. To satisfy the first objective, three different tibiofemoral models were incorporated into an existing forward dynamic simulation of recumbent pedaling, and the resulting kinematics, pedal forces, muscle forces, and joint reaction forces were compared. Two of these models replicated the rolling and sliding motion of the tibia on the femur, while the third was a simple pin joint. To satisfy the second objective, variations in the most widely used of the three knee models were created by adjusting the experimental data used in the development of this model. These variations were incorporated into the pedaling simulation, and the resulting data were compared with the unaltered model. Differences between the two rolling-sliding models were smaller than differences between the pin-joint model and the rolling-sliding models. Joint reactions forces, particularly at the knee, were highly sensitive to changes in knee joint model kinematics, as high as 61% root mean squared difference, normalized by the corresponding peak force of the unaltered reference model. Muscle forces were also sensitive, as high as 30% root mean squared difference. Muscle excitations were less sensitive. The observed changes in muscle force and joint reaction forces were caused primarily by changes in the moment arms and musculotendon lengths of the quadriceps. Although some level of inaccuracy in the knee model may be acceptable for calculations of muscle excitation timing, a representative model of knee kinematics is necessary for accurate calculation of muscle and joint reaction forces.

Marker-Based Reconstruction of the Kinematics of a Chain of Segments: A New Method That Incorporates Joint Kinematic Constraints

The aim of skin-marker-based motion analysis is to reconstruct the motion of a kinematical model from noisy measured motion of skin markers. Existing kinematic models for reconstruction of chains of segments can be divided into two categories: analytical methods that do not take joint constraints into account and numerical global optimization methods that do take joint constraints into account but require numerical optimization of a large number of degrees of freedom, especially when the number of segments increases. In this study, a new and largely analytical method for a chain of rigid bodies is presented, interconnected in spherical joints (chain-method). In this method, the number of generalized coordinates to be determined through numerical optimization is three, irrespective of the number of segments. This new method is compared with the analytical method of Veldpaus et al. [1988, “A Least-Squares Algorithm for the Equiform Transformation From Spatial Marker Co-Ordinates,” J. Biomech., 21, pp. 45–54] (Veldpaus-method, a method of the first category) and the numerical global optimization method of Lu and O’Connor [1999, “Bone Position Estimation From Skin-Marker Co-Ordinates Using Global Optimization With Joint Constraints,” J. Biomech., 32, pp. 129–134] (Lu-method, a method of the second category) regarding the effects of continuous noise simulating skin movement artifacts and regarding systematic errors in joint constraints. The study is based on simulated data to allow a comparison of the results of the different algorithms with true (noise- and error-free) marker locations. Results indicate a clear trend that accuracy for the chain-method is higher than the Veldpaus-method and similar to the Lu-method. Because large parts of the equations in the chain-method can be solved analytically, the speed of convergence in this method is substantially higher than in the Lu-method. With only three segments, the average number of required iterations with the chain-method is 3.0±0.2 times lower than with the Lu-method when skin movement artifacts are simulated by applying a continuous noise model. When simulating systematic errors in joint constraints, the number of iterations for the chain-method was almost a factor 5 lower than the number of iterations for the Lu-method. However, the Lu-method performs slightly better than the chain-method. The RMSD value between the reconstructed and actual marker positions is approximately 57% of the systematic error on the joint center positions for the Lu-method compared with 59% for the chain-method.

An Automated Image-Based Method of 3D Subject-Specific Body Segment Parameter Estimation for Kinetic Analyses of Rapid Movements

Accurate subject-specific body segment parameters (BSPs) are necessary to perform kinetic analyses of human movements with large accelerations, or no external contact forces or moments. A new automated topographical image-based method of estimating segment mass, center of mass (CM) position, and moments of inertia is presented. Body geometry and volume were measured using a laser scanner, then an automated pose and shape registration algorithm segmented the scanned body surface, and identified joint center (JC) positions. Assuming the constant segment densities of Dempster, thigh and shank masses, CM locations, and moments of inertia were estimated for four male subjects with body mass indexes (BMIs) of 19.7–38.2. The subject-specific BSP were compared with those determined using Dempster and Clauser regression equations. The influence of BSP and BMI differences on knee and hip net forces and moments during a running swing phase were quantified for the subjects with the smallest and largest BMIs. Subject-specific BSP for 15 body segments were quickly calculated using the image-based method, and total subject masses were overestimated by 1.7–2.9%.When compared with the Dempster and Clauser methods, image-based and regression estimated thigh BSP varied more than the shank parameters. Thigh masses and hip JC to thigh CM distances were consistently larger, and each transverse moment of inertia was smaller using the image-based method. Because the shank had larger linear and angular accelerations than the thigh during the running swing phase, shank BSP differences had a larger effect on calculated intersegmental forces and moments at the knee joint than thigh BSP differences did at the hip. It was the net knee kinetic differences caused by the shank BSP differences that were the largest contributors to the hip variations. Finally, BSP differences produced larger kinetic differences for the subject with larger segment masses, suggesting that parameter accuracy is more important for studies focused on overweight populations. The new image-based BSP estimation method described in this paper addressed the limitations of currently used geometric and regression methods by using exact limb geometry to determine subject-specific parameters. BSP differences have the largest effect on kinetic analyses of motions with large limb accelerations, for joints farther along the kinematic chain from the known forces and moments, and for subjects with larger limb masses or BMIs.

Design of Optimal Treatments for Neuromusculoskeletal Disorders using Patient-Specific Multibody Dynamic Models

Disorders of the human neuromusculoskeletal system such as osteoarthritis, stroke, cerebral palsy, and paraplegia significantly affect mobility and result in a decreased quality of life. Surgical and rehabilitation treatment planning for these disorders is based primarily on static anatomic measurements and dynamic functional measurements filtered through clinical experience. While this subjective treatment planning approach works well in many cases, it does not predict accurate functional outcome in many others. This paper presents a vision for how patient-specific multibody dynamic models can serve as the foundation for an objective treatment planning approach that identifies optimal treatments and treatment parameters on an individual patient basis. First, a computational paradigm is presented for constructing patient-specific multibody dynamic models. This paradigm involves a combination of patient-specific skeletal models, muscle-tendon models, neural control models, and articular contact models, with the complexity of the complete model being dictated by the requirements of the clinical problem being addressed. Next, three clinical applications are presented to illustrate how such models could be used in the treatment design process. One application involves the design of patient-specific gait modification strategies for knee osteoarthritis rehabilitation, a second involves the selection of optimal patient-specific surgical parameters for a particular knee osteoarthritis surgery, and the third involves the design of patient-specific muscle stimulation patterns for stroke rehabilitation. The paper concludes by discussing important challenges that need to be overcome to turn this vision into reality.

Optimal estimation of dynamically consistent kinematics and kinetics for forward dynamic simulation of gait

Forward dynamic simulation provides a powerful framework for characterizing internal loads and for predicting changes in movement due to injury, impairment or surgical intervention. However, the computational challenge of generating simulations has greatly limited the use and application of forward dynamic models for simulating human gait. In this study, we introduce an optimal estimation approach to efficiently solve for generalized accelerations that satisfy the overall equations of motion and best agree with measured kinematics and ground reaction forces. The estimated accelerations are numerically integrated to enforce dynamic consistency over time, resulting in a forward dynamic simulation. Numerical optimization is then used to determine a set of initial generalized coordinates and speeds that produce a simulation that is most consistent with the measured motion over a full cycle of gait. The proposed method was evaluated with synthetically created kinematics and force plate data in which both random noise and bias errors were introduced. We also applied the method to experimental gait data collected from five young healthy adults walking at a preferred speed. We show that the proposed residual elimination algorithm (REA) converges to an accurate solution, reduces the detrimental effects of kinematic measurement errors on joint moments, and eliminates the need for residual forces that arise in standard inverse dynamics. The greatest improvements in joint kinetics were observed proximally, with the algorithm reducing joint moment errors due to marker noise by over 20% at the hip and over 50% at the low back. Simulated joint angles were generally within 1 deg of recorded values when REA was used to generate a simulation from experimental gait data. REA can thus be used as a basis for generating accurate simulations of subject-specific gait dynamics.

Foot motions in manual material handling transfer tasks: a taxonomy and data from an automotive assembly plant

Ergonomic job analysis commonly applies static postural and biomechanical analysis tools to particular postures observed during manual material handling (MMH) tasks, usually focusing on the most extreme postures or those involving the highest loads. When these analyses are conducted prospectively using digital human models, accurate prediction of the foot placements is critical to realistic postural analyses. In automotive assembly jobs, workers frequently take several steps between task elements, for example, picking up a part at one location and moving to another location to place it on the vehicle. A detailed understanding of the influence of task type and task sequence on the stepping pattern is necessary to accurately predict the foot placements associated with MMH tasks. The current study examined the patterns of foot motions observed during automotive assembly tasks. Video data for 529 pickup and delivery tasks from 32 automotive assembly jobs were analysed. A minimum of five cycles was analysed for each task. The approach angle, departure angle, hand(s) used, manipulation height and patterns of footsteps were coded from the video. Object mass was identified from the job information sheet provided by the assembly plant. Three independent raters coded each video and demonstrated an intraclass correlation coefficient of 0.54 for identification of the configuration of the lower extremities during terminal stance. Based on an analysis of the distribution of stepping behaviours during object transitions (pickups or deliveries), a transition classification system (TRACS) was developed. TRACS uses a compact notation to quantify the sequence of steps associated with a MMH transition. Five TRACS behaviour groups accounted for over 90% of the transition stepping behaviours observed in the assembly plant. Approximately two-thirds (68.4%) of the object transfers observed were performed with only one foot in contact with the ground during the terminal posture. The results from this paper suggest that a predictive model for choosing a transition stepping behaviour, coupled with a model to scale the selected foot behaviours, is needed to facilitate accurate prospective ergonomic analyses. This study proposes a method for categorising the stepping patterns associated with MMH tasks. The influence of task type and task sequence on the stepping patterns observed during several automotive assembly tasks is discussed. For prospective postural analyses conducted using digital human models, accurate prediction of the foot placements is critical to realistic postural analyses.