A comprehensive dataset on biomechanics and motor control during human walking with discrete mechanical perturbations

Background

Humans have a remarkable capability to maintain balance while walking. There is, however, a lack of publicly available research data on reactive responses to destabilizing perturbations during gait.

Methods

Here, we share a comprehensive dataset collected from 10 participants who experienced random perturbations while walking on an instrumented treadmill. Each participant performed six 5-min walking trials at a rate of 1.2 m/s, during which rapid belt speed perturbations could occur during the participant's stance phase. Each gait cycle had a 17% probability of being perturbed. The perturbations consisted of an increase of belt speed by 0.75 m/s, delivered with equal probability at 10%, 20%, 30%, 40%, 50%, 60%, 70%, or 80% of the stance phase. Data were recorded using motion capture with 25 markers, eight inertial measurement units (IMUs), and electromyography (EMG) from the tibialis anterior (TA), soleus (SOL), lateral gastrocnemius (LG), rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM), biceps femoris (BF), and gluteus maximus (GM). The full protocol is described in detail.

Results

We provide marker trajectories, force plate data, EMG data, and belt speed information for all trials and participants. IMU data is provided for most participants. This data can be useful for identifying neural feedback control in human gait, biologically inspired control systems for robots, and the development of clinical applications.

Model-based estimation of muscle and ACL forces during turning maneuvers in alpine skiing

In alpine skiing, estimation of the muscle forces and joint loads such as the forces in the ACL of the knee are essential to quantify the loading pattern of the skier during turning maneuvers. Since direct measurement of these forces is generally not feasible, non-invasive methods based on musculoskeletal modeling should be considered. In alpine skiing, however, muscle forces and ACL forces have not been analyzed during turning maneuvers due to the lack of three dimensional musculoskeletal models. In the present study, a three dimensional musculoskeletal skier model was successfully applied to track experimental data of a professional skier. During the turning maneuver, the primary activated muscles groups of the outside leg, bearing the highest loads, were the gluteus maximus, vastus lateralis as well as the medial and lateral hamstrings. The main function of these muscles was to generate the required hip extension and knee extension moments. The gluteus maximus was also the main contributor to the hip abduction moment when the hip was highly flexed. Furthermore, the lateral hamstrings and gluteus maximus contributed to the hip external rotation moment in addition to the quadratus femoris. Peak ACL forces reached 211 N on the outside leg with the main contribution in the frontal plane due to an external knee abduction moment. Sagittal plane contributions were low due to consistently high knee flexion (> 60[Formula: see text]), substantial co-activation of the hamstrings and the ground reaction force pushing the anteriorly inclined tibia backwards with respect to the femur. In conclusion, the present musculoskeletal simulation model provides a detailed insight into the loading of a skier during turning maneuvers that might be used to analyze appropriate training loads or injury risk factors such as the speed or turn radius of the skier, changes of the equipment or neuromuscular control parameters.

Predicting neuromuscular control patterns that minimize ACL forces during injury prone jump landing maneuvers in downhill skiing using a musculoskeletal simulation model

Competitive skiers encounter a high risk of sustaining an ACL injury during jump-landing in downhill ski racing. Facing an injury-prone landing manoeuvre, there is a lack of knowledge regarding optimum control strategies. So, the purpose of the present study was to investigate possible neuromuscular control patterns to avoid injury during injury-prone jump-landing manoeuvres. A computational approach was used to generate a series of 190 injury-prone jump-landing manoeuvres based on a 25-degree-of-freedom sagittal plane musculoskeletal skier model. Using a dynamic optimization framework, each injury-prone landing manoeuvre was resolved to identify muscle activation patterns of the lower limbs and corresponding kinematic changes that reduce peak ACL force. In the 190 injury-prone jump-landing simulations, ACL forces peaked during the first 50 ms after ground contact. Optimized muscle activation patterns, that reduced peak ACL forces, showed increased activation of the monoarticular hip flexors, ankle dorsi- and plantar flexors as well as hamstrings prior to or during the early impact phase (<50 ms). The corresponding kinematic changes were characterized by increased hip and knee flexion and less backward lean of the skier at initial ground contact and the following impact phase. Injury prevention strategies should focus on increased activation of the monoarticular hip flexors, ankle plantar flexors and rapid and increased activation of the hamstrings in combination with a flexed landing position and decreased backward lean to reduce ACL injury risk during the early impact phase (<50 ms) of jump landing.HighlightsFirst study investigating advantageous control strategies during injury-prone jump-landing manoeuvres in downhill skiing using a musculoskeletal simulation model and dynamic optimization framework.The simulation results predicted high injury risk during the first 50 ms after initial ground contact.Optimized neuromuscular control patterns showed adapted activation patterns (timing and amplitude) of muscles crossing the knee as well as the hip and ankle joints prior to and after initial ground contact, respectively.An optimized control strategy during an injury-prone landing manoeuvre was characterized kinematically by increasing hip and knee flexion and less backward lean of the skier at initial ground contact and the following impact phase.

An Anthropometrically Parameterized Assistive Lower Limb Exoskeleton

This paper presents an innovative design methodology for development of lower limb exoskeletons with the fabrication and experimental evaluation of prototype hardware. The proposed design approach is specifically conceived to be suitable for the pediatric population and uses additive manufacturing and a model parameterized in terms of subject anthropometrics to give a person-specific custom fit. The methodology is applied to create computer-aided design models using average anthropometrics of children 6-11 years old and using anthropometrics of an individual measured by the researchers. This demonstrates that the approach can scale to subject weight and height. A prototype exoskeleton is fabricated, which can actuate the hip and knee joints without restricting hip abduction-adduction motion. In order to test usability of the device and evaluate walking assistance, user effort is quantified in an assisted condition where the subject walks on a level treadmill with the exoskeleton powered. This is compared to an unassisted condition with the exoskeleton unpowered and a baseline condition with the subject not wearing the exoskeleton. Comparing assisted to baseline conditions, torque magnitudes increased at the hip and knee, mechanical energy generated increased at the hip but decreased at the knee, and muscle activations increased in the Vastus Lateralis but decreased in the Biceps Femoris. While the preliminary evidence for walking assistance is not entirely convincing for the tested conditions, the presented design methodology itself is promising as it has been successfully validated through the creation of computer-aided design models for children and fabrication of a serviceable exoskeleton prototype.

A progressive-individualized midstance gait perturbation protocol for reactive balance assessment in stroke survivors

Restoration of balance control is a primary focus of rehabilitation after a stroke. The study developed a gait perturbation, treadmill-based, balance assessment protocol and demonstrated that it can be used to quantify improvements in reactive balance responses among individuals post-stroke. The protocol consists of a sequence of fifteen 90-second treadmill walking trials, with a single perturbation applied during the middle third of each trial. Gait was perturbed by rapid acceleration-deceleration of the treadmill belt at mid-stance of the unaffected leg during a randomly selected gait cycle. The initial perturbation magnitude was based on the participant's maximum walking speed and increased or decreased in each trial, based on success or failure of recovery, as determined from an instrumented harness. The protocol was used before and after a 10-week period of therapy in twenty-four stroke survivors. Outcomes included maximum recoverable perturbation (MRP), self-selected gait speed, levels progressed through the algorithm, and falls versus recoveries.Participants were able to take recovery steps in response to the perturbation. Twelve participants completed the full assessment protocol before and after the therapeutic intervention. After the intervention, they had fewer falls and more recoveries (p < 0.001), progressed through more algorithm levels (p = 0.043), had a higher MRP (p = 0.005), and had higher gait speeds. The protocol was found to be feasible in stroke survivors with moderate gait deficits. The data supports the conclusion that this protocol can be used in clinical research to quantify improvements in balance during walking.

Identification of Postural Controllers in Human Standing Balance

Standing balance is a simple motion task for healthy humans but the actions of the central nervous system (CNS) have not been described by generalized and sufficiently sophisticated control laws. While system identification approaches have been used to extracted models of the CNS, they either focus on short balance motions, leading to task-specific control laws, or assume that the standing balance system is linear. To obtain comprehensive control laws for human standing balance, complex balance motions, long duration tests, and nonlinear controller models are all needed. In this paper, we demonstrate that trajectory optimization with the direct collocation method can achieve these goals to identify complex CNS models for the human standing balance task. We first examined this identification method using synthetic motion data and showed that correct control parameters can be extracted. Then, six types of controllers, from simple linear to complex nonlinear, were identified from 100 s of motion data from randomly perturbed standing. Results showed that multiple time-delay paths and nonlinear properties are both needed in order to fully explain human feedback control of standing balance.

A model-based approach to predict neuromuscular control patterns that minimize ACL forces during jump landing

Jump landing is a common situation leading to knee injuries involving the anterior cruciate ligament (ACL) in sports. Although neuromuscular control is considered as a key injury risk factor, there is a lack of knowledge regarding optimum control strategies that reduce ACL forces during jump landing. In the present study, a musculoskeletal model-based computational approach is presented that allows identifying neuromuscular control patterns that minimize ACL forces during jump landing. The approach is demonstrated for a jump landing maneuver in downhill skiing, which is one out of three main injury mechanisms in competitive skiing.

CNN-Based Estimation of Sagittal Plane Walking and Running Biomechanics From Measured and Simulated Inertial Sensor Data

Machine learning is a promising approach to evaluate human movement based on wearable sensor data. A representative dataset for training data-driven models is crucial to ensure that the model generalizes well to unseen data. However, the acquisition of sufficient data is time-consuming and often infeasible. We present a method to create realistic inertial sensor data with corresponding biomechanical variables by 2D walking and running simulations. We augmented a measured inertial sensor dataset with simulated data for the training of convolutional neural networks to estimate sagittal plane joint angles, joint moments, and ground reaction forces (GRFs) of walking and running. When adding simulated data, the root mean square error (RMSE) of the test set of hip, knee, and ankle joint angles decreased up to 17%, 27% and 23%, the RMSE of knee and ankle joint moments up to 6% and the RMSE of anterior-posterior and vertical GRF up to 2 and 6%. Simulation-aided estimation of joint moments and GRFs was limited by inaccuracies of the biomechanical model. Improving the physics-based model and domain adaptation learning may further increase the benefit of simulated data. Future work can exploit biomechanical simulations to connect different data sources in order to create representative datasets of human movement. In conclusion, machine learning can benefit from available domain knowledge on biomechanical simulations to supplement cumbersome data collections.

A solution method for predictive simulations in a stochastic environment

Predictive gait simulations currently do not account for environmental or internal noise. We describe a method to solve predictive simulations of human movements in a stochastic environment using a collocation method. The optimization is performed over multiple noisy episodes of the trajectory, instead of a single episode in a deterministic environment. Each episode used the same control parameters. The method was verified on a torque-driven pendulum swing-up problem. A different optimal trajectory was found in a stochastic environment than in the deterministic environment. Next, it was applied to gait to show its application in predictive simulation of human movement. We show that, unlike in a deterministic model, a nonzero minimum foot clearance during swing is predicted by a minimum-effort criterion in a stochastic environment. The predicted amount of foot clearance increased with the noise amplitude.

Identification of the human postural control system through stochastic trajectory optimization

BACKGROUND

System identification can be used to obtain a model of the human postural control system from experimental data in which subjects are mechanically perturbed while standing. However, unstable controllers were sometimes found, which obviously do not explain human balance and cannot be applied in control of humanoid robots. Eigenvalue constraints can be used to avoid unstable controllers. However, this method is hard to apply to highly nonlinear systems and large identification datasets.

NEW METHOD

To address these issues, we perform the system identification with a stochastic system model where process noise is modeled. The parameter identification is performed by simultaneous trajectory optimizations on multiple episodes that have different instances of the process noise.

RESULTS

The stochastic and deterministic identification methods were tested on three types of controllers, including both linear and nonlinear controller architectures. Stochastic identification tracked the experimental data nearly as well as the deterministic identification, while avoiding the unstable controllers that were found with a deterministic system model.

COMPARISON WITH EXISTING METHOD

Comparing to eigenvalue constraints, stochastic identification has wider application potentials. Since linearization is not needed in the stochastic identification, it is applicable to highly nonlinear systems, and it can be applied on large data-sets.

CONCLUSIONS

Stochastic identification can be used to avoid unstable controllers in human postural control identification.

ISB recommendations on the reporting of intersegmental forces and moments during human motion analysis

The International Society of Biomechanics (ISB) has charged this committee with development of a standard similar in scope to the kinematic standard proposed in Wu et al. (2002) and Wu et al. (2005). Given the variety of purposes for which intersegmental forces and moments are used in biomechanical research, it is not possible to recommend a particular set of analysis standards that will be acceptable in all applications. Instead, it is the purpose of this paper to recommend a set of reporting standards that will result in an understanding of the differences between investigations and the ability to reproduce the research. The end products of this standard are (1) a critical checklist that can be used during submission of manuscripts and abstracts to insure adequate description of methods, and (2) a web based visualization tool that can be used to alter the coordinate system, normalization technique and internal/external perspective of intersegmental forces and moments during walking and running so that the shape and magnitude of the curves can be compared to one's own data.

Metabolic cost calculations of gait using musculoskeletal energy models, a comparison study

This paper compares predictions of metabolic energy expenditure in gait using seven metabolic energy expenditure models to assess their correlation with experimental data. Ground reaction forces, marker data, and pulmonary gas exchange data were recorded for six walking trials at combinations of two speeds, 0.8 m/s and 1.3 m/s, and three inclines, -8% (downhill), level, and 8% (uphill). The metabolic cost, calculated with the metabolic energy models was compared to the metabolic cost from the pulmonary gas exchange rates. A repeated measures correlation showed that all models correlated well with experimental data, with correlations of at least 0.9. The model by Bhargava et al. (J Biomech, 2004: 81-88) and the model by Lichtwark and Wilson (J Exp Biol, 2005: 2831-3843) had the highest correlation, 0.95. The model by Margaria (Int Z Angew Physiol Einschl Arbeitsphysiol, 1968: 339-351) predicted the increase in metabolic cost following a change in dynamics best in absolute terms.

Optimal control simulation predicts effects of midsole materials on energy cost of running

Testing sports equipment with athletes is costly, time-consuming, hazardous and sometimes impracticable. We propose a method for virtual testing of running shoes and predict how midsoles made of BOOST^TM affect energy cost of running. We contribute a visco-elastic contact model and identified model parameters based on load-displacement measurements. We propose a virtual study using optimal control simulation of musculoskeletal models. The predicted reduction in energy cost of ∼1% for BOOST^TM in comparison to conventional materials is consistent with experimental studies. This indicates that the proposed method is capable of replacing experimental studies in the future.

Simulation Analysis of Linear Quadratic Regulator Control of Sagittal-Plane Human Walking-Implications for Exoskeletons

The linear quadratic regulator (LQR) is a classical optimal control approach that can regulate gait dynamics about target kinematic trajectories. Exoskeletons to restore gait function have conventionally utilized time-varying proportional-derivative (PD) control of leg joints. But, these PD parameters are not uniquely optimized for whole-body (full-state) performance. The objective of this study was to investigate the effectiveness of LQR full-state feedback compared to PD control to maintain bipedal walking of a sagittal-plane computational model against force disturbances. Several LQR controllers were uniquely solved with feedback gains optimized for different levels of tracking capability versus control effort. The main implications to future exoskeleton control systems include (1) which LQR controllers out-perform PD controllers in walking maintenance and effort, (2) verifying that LQR desirably produces joint torques that oppose rapidly growing joint state errors, and (3) potentially equipping accurate sensing systems for nonjoint states such as hip-position and torso orientation. The LQR controllers capable of longer walk times than respective PD controllers also required less control effort. During sudden leg collapse, LQR desirably behaved like PD by generating feedback torques that opposed the direction of leg-joint errors. Feedback from nonjoint states contributed to over 50% of the LQR joint torques and appear critical for whole-body LQR control. While LQR control poses implementation challenges, such as more sensors for full-state feedback and operation near the desired trajectories, it offers significant performance advantages over PD control.

OpenSim Versus Human Body Model: A Comparison Study for the Lower Limbs During Gait

Musculoskeletal modeling and simulations have become popular tools for analyzing human movements. However, end users are often not aware of underlying modeling and computational assumptions. This study investigates how these assumptions affect biomechanical gait analysis outcomes performed with Human Body Model and the OpenSim gait2392 model. The authors compared joint kinematics, kinetics, and muscle forces resulting from processing data from 7 healthy adults with both models. Although outcome variables had similar patterns, there were statistically significant differences in joint kinematics (maximal difference: 9.8° [1.5°] in sagittal plane hip rotation), kinetics (maximal difference: 0.36 [0.10] N·m/kg in sagittal plane hip moment), and muscle forces (maximal difference: 8.51 [1.80] N/kg for psoas). These differences might be explained by differences in hip and knee joint center locations up to 2.4 (0.5) and 1.9 (0.2) cm in the posteroanterior and inferosuperior directions, respectively, and by the offset in pelvic reference frames of about 10° around the mediolateral axis. The choice of model may not influence the conclusions in clinical settings, where the focus is on interpreting deviations from the reference data, but it will affect the conclusions of mechanical analyses in which the goal is to obtain accurate estimates of kinematics and loading.

A metabolic energy expenditure model with a continuous first derivative and its application to predictive simulations of gait

Whether humans minimize metabolic energy in gait is unknown. Gradient-based optimization could be used to predict gait without using walking data but requires a twice differentiable metabolic energy model. Therefore, the metabolic energy model of Umberger et al. ( 2003 ) was adapted to be twice differentiable. Predictive simulations of a reaching task and gait were solved using this continuous model and by minimizing effort. The reaching task simulation showed that energy minimization predicts unrealistic movements when compared to effort minimization. The predictive gait simulations showed that objectives other than metabolic energy are also important in gait.

Peak ACL force during jump landing in downhill skiing is less sensitive to landing height than landing position

Background

Competitive skiers face a high risk of sustaining an ACL injury during jump landing in downhill skiing. There is a lack of knowledge on how landing height affects this risk.

Objectives

To evaluate the effect of varied landing height on peak ACL force during jump landing and to compare the effect of the landing height with the effect of the landing position varied by the trunk lean of the skier.

Methods

A 25-degree-of-freedom sagittal plane musculoskeletal model of an alpine skier, accompanied by a dynamic optimisation framework, was used to simulate jump landing manoeuvres in downhill skiing. First, a reference simulation was computed tracking experimental data of competitive downhill skier performing a jump landing manoeuvre. Second, sensitivity studies were performed computing 441 landing manoeuvres with perturbed landing height and trunk lean of the skier, and the corresponding effects on peak ACL force were determined.

Results

The sensitivity studies revealed that peak ACL force increased with jump height and backward lean of the skier as expected. However, peak ACL was about eight times more sensitive to the trunk lean of the skier compared with landing height. The decreased sensitivity of the landing height was based on the lower effects on the knee muscle forces and the shear component of the knee joint reaction force.

Conclusion

Preventive measures are suggested to focus primarily on avoiding trunk backward lean of the skier, and consequently on proper jump preparation and technique, and secondarily on strategies to reduce landing height during jumps.

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.

Optimal design and control of an electromechanical transfemoral prosthesis with energy regeneration

In this paper, we present the design of an electromechanical above-knee active prosthesis with energy storage and regeneration. The system consists of geared knee and ankle motors, parallel springs for each motor, an ultracapacitor, and controllable four-quadrant power converters. The goal is to maximize the performance of the system by finding optimal controls and design parameters. A model of the system dynamics was developed, and used to solve a combined trajectory and design optimization problem. The objectives of the optimization were to minimize tracking error relative to human joint motions, as well as energy use. The optimization problem was solved by the method of direct collocation, based on joint torque and joint angle data from ten subjects walking at three speeds. After optimization of controls and design parameters, the simulated system could operate at zero energy cost while still closely emulating able-bodied gait. This was achieved by controlled energy transfer between knee and ankle, and by controlled storage and release of energy throughout the gait cycle. Optimal gear ratios and spring parameters were similar across subjects and walking speeds.

Training an Actor-Critic Reinforcement Learning Controller for Arm Movement Using Human-Generated Rewards

Functional Electrical Stimulation (FES) employs neuroprostheses to apply electrical current to the nerves and muscles of individuals paralyzed by spinal cord injury to restore voluntary movement. Neuroprosthesis controllers calculate stimulation patterns to produce desired actions. To date, no existing controller is able to efficiently adapt its control strategy to the wide range of possible physiological arm characteristics, reaching movements, and user preferences that vary over time. Reinforcement learning (RL) is a control strategy that can incorporate human reward signals as inputs to allow human users to shape controller behavior. In this paper, ten neurologically intact human participants assigned subjective numerical rewards to train RL controllers, evaluating animations of goal-oriented reaching tasks performed using a planar musculoskeletal human arm simulation. The RL controller learning achieved using human trainers was compared with learning accomplished using human-like rewards generated by an algorithm; metrics included success at reaching the specified target; time required to reach the target; and target overshoot. Both sets of controllers learned efficiently and with minimal differences, significantly outperforming standard controllers. Reward positivity and consistency were found to be unrelated to learning success. These results suggest that human rewards can be used effectively to train RL-based FES controllers.

Standardization proposal of soft tissue artefact description for data sharing in human motion measurements

Soft tissue artefact (STA) represents one of the main obstacles for obtaining accurate and reliable skeletal kinematics from motion capture. Many studies have addressed this issue, yet there is no consensus on the best available bone pose estimator and the expected errors associated with relevant results. Furthermore, results obtained by different authors are difficult to compare due to the high variability and specificity of the phenomenon and the different metrics used to represent these data. Therefore, the aim of this study was twofold: firstly, to propose standards for description of STA; and secondly, to provide illustrative STA data samples for body segments in the upper and lower extremities and for a range of motor tasks specifically, level walking, stair ascent, sit-to-stand, hip- and knee-joint functional movements, cutting motion, running, hopping, arm elevation and functional upper-limb movements. The STA dataset includes motion of the skin markers measured in vivo and ex vivo using stereophotogrammetry as well as motion of the underlying bones measured using invasive or bio-imaging techniques (i.e., X-ray fluoroscopy or MRI). The data are accompanied by a detailed description of the methods used for their acquisition, with information given about their quality as well as characterization of the STA using the proposed standards. The availability of open-access and standard-format STA data will be useful for the evaluation and development of bone pose estimators thus contributing to the advancement of three-dimensional human movement analysis and its translation into the clinical practice and other applications.

Real-time simulation of hand motion for prosthesis control

Individuals with hand amputation suffer substantial loss of independence. Performance of sophisticated prostheses is limited by the ability to control them. To achieve natural and simultaneous control of all wrist and hand motions, we propose to use real-time biomechanical simulation to map between residual EMG and motions of the intact hand. Here we describe a musculoskeletal model of the hand using only extrinsic muscles to determine whether real-time performance is possible. Simulation is 1.3 times faster than real time, but the model is locally unstable. Methods are discussed to increase stability and make this approach suitable for prosthesis control.

An optimized proportional-derivative controller for the human upper extremity with gravity

When Functional Electrical Stimulation (FES) is used to restore movement in subjects with spinal cord injury (SCI), muscle stimulation patterns should be selected to generate accurate and efficient movements. Ideally, the controller for such a neuroprosthesis will have the simplest architecture possible, to facilitate translation into a clinical setting. In this study, we used the simulated annealing algorithm to optimize two proportional-derivative (PD) feedback controller gain sets for a 3-dimensional arm model that includes musculoskeletal dynamics and has 5 degrees of freedom and 22 muscles, performing goal-oriented reaching movements. Controller gains were optimized by minimizing a weighted sum of position errors, orientation errors, and muscle activations. After optimization, gain performance was evaluated on the basis of accuracy and efficiency of reaching movements, along with three other benchmark gain sets not optimized for our system, on a large set of dynamic reaching movements for which the controllers had not been optimized, to test ability to generalize. Robustness in the presence of weakened muscles was also tested. The two optimized gain sets were found to have very similar performance to each other on all metrics, and to exhibit significantly better accuracy, compared with the three standard gain sets. All gain sets investigated used physiologically acceptable amounts of muscular activation. It was concluded that optimization can yield significant improvements in controller performance while still maintaining muscular efficiency, and that optimization should be considered as a strategy for future neuroprosthesis controller design.

Comparison of two methods of determining patellofemoral joint stress during dynamic activities

BACKGROUND

Joint specific models rely on muscle force estimates to quantify tissue specific stresses. Traditionally, muscle forces have been estimated using inverse dynamics alone. Inverse dynamics coupled with static optimization techniques allow for an alternative method in estimating muscle forces. Differences between these two techniques have not been compared for determining the quadriceps force for estimating patellofemoral joint stress.

METHODS

Eleven female participants completed five squats and ten running trials. Motion capture and force platform data were processed using both solely inverse dynamics and inverse dynamics with static optimization to estimate the quadriceps force in a patellofemoral joint model.

FINDINGS

Patellofemoral joint stress calculations were consistently higher when using the combination of inverse dynamics and static optimization as compared to the inverse dynamics alone (p<0.05) yielding estimates that were 30-106% greater.

INTERPRETATION

When implementing joint models to estimate tissue specific stresses, the choice of technique used to estimate muscle forces plays an important role in determining the magnitude of estimated stresses in patellofemoral joint models.

An elaborate data set on human gait and the effect of mechanical perturbations

Here we share a rich gait data set collected from fifteen subjects walking at three speeds on an instrumented treadmill. Each trial consists of 120 s of normal walking and 480 s of walking while being longitudinally perturbed during each stance phase with pseudo-random fluctuations in the speed of the treadmill belt. A total of approximately 1.5 h of normal walking (>5000 gait cycles) and 6 h of perturbed walking (>20,000 gait cycles) is included in the data set. We provide full body marker trajectories and ground reaction loads in addition to a presentation of processed data that includes gait events, 2D joint angles, angular rates, and joint torques along with the open source software used for the computations. The protocol is described in detail and supported with additional elaborate meta data for each trial. This data can likely be useful for validating or generating mathematical models that are capable of simulating normal periodic gait and non-periodic, perturbed gaits.

The contribution of the acetabular labrum to hip joint stability: a quantitative analysis using a dynamic three-dimensional robot model

The acetabular labrum provides mechanical stability to the hip joint in extreme positions where the femoral head is disposed to subluxation. We aimed to quantify the isolated labrum's stabilizing value. Five human cadaveric hips were mounted to a robotic manipulator, and subluxation potential tests were run with and without labrum. Three-dimensional (3D) kinematic data were quantified using the stability index (Colbrunn et al., 2013, "Impingement and Stability of Total Hip Arthroplasty Versus Femoral Head Resurfacing Using a Cadaveric Robotics Model," J. Orthop. Res., 31(7), pp. 1108-1115). Global and regional stability indices were significantly greater with labrum intact than after total labrectomy for both anterior and posterior provocative positions. In extreme positions, the labrum imparts significant overall mechanical resistance to hip subluxation. Regional stability contributions vary with joint orientation.

Real-time simulation of three-dimensional shoulder girdle and arm dynamics

Electrical stimulation is a promising technology for the restoration of arm function in paralyzed individuals. Control of the paralyzed arm under electrical stimulation, however, is a challenging problem that requires advanced controllers and command interfaces for the user. A real-time model describing the complex dynamics of the arm would allow user-in-the-loop type experiments where the command interface and controller could be assessed. Real-time models of the arm previously described have not included the ability to model the independently controlled scapula and clavicle, limiting their utility for clinical applications of this nature. The goal of this study therefore was to evaluate the performance and mechanical behavior of a real-time, dynamic model of the arm and shoulder girdle. The model comprises seven segments linked by eleven degrees of freedom and actuated by 138 muscle elements. Polynomials were generated to describe the muscle lines of action to reduce computation time, and an implicit, first-order Rosenbrock formulation of the equations of motion was used to increase simulation step-size. The model simulated flexion of the arm faster than real time, simulation time being 92% of actual movement time on standard desktop hardware. Modeled maximum isometric torque values agreed well with values from the literature, showing that the model simulates the moment-generating behavior of a real human arm. The speed of the model enables experiments where the user controls the virtual arm and receives visual feedback in real time. The ability to optimize potential solutions in simulation greatly reduces the burden on the user during development.

Inertial compensation for belt acceleration in an instrumented treadmill

Instrumented treadmills provide a convenient means for applying horizontal perturbations during gait or standing. However, varying the treadmill belt speed introduces inertial artifacts in the sagittal plane moment component of the ground reaction force. Here we present a compensation method based on a second-order dynamic model that predicts inertial pitch moment from belt acceleration. The method was tested experimentally on an unloaded treadmill at a slow belt speed with small random variations (1.20±0.10m/s) and at a faster belt speed with large random variations (2.00±0.50m/s). Inertial artifacts of up to 12Nm (root-mean-square, RMS) and 30Nm (peak) were observed. Coefficients of the model were calibrated on one trial and then used to predict and compensate the pitch moment of another trial with different random variations. Coefficients of determination (R(2)) were 72.08% and 96.75% for the slow and fast conditions, respectively. After compensation, the root-mean-square (RMS) of the inertial artifact was reduced by 47.37% for the slow speed and 81.98% for fast speed, leaving only 1.5Nm and 2.1Nm of artifact uncorrected, respectively. It was concluded that the compensation technique reduced inertial errors substantially, thereby improving the accuracy in joint moment calculations on an instrumented treadmill with varying belt speed.

Development of dynamic models of the Mauch prosthetic knee for prospective gait simulation

Recent advances in computational modeling and simulation of human movement makes it possible to isolate and predict the potential contributions of a prosthetic device to the overall system performance. The Mauch S-N-S knee is one of the most widely used prosthetic knees in the market. The goal of this study is to develop dynamic models of the Mauch S-N-S knee for predictive simulation of a transfemoral amputee's gait under idealized conditions. Based on the functional description of the Mauch S-N-S prosthetic knee from the literature, a combined bench test and data fitting approach employing modified slow, normal, and fast gait patterns and nine combinations of stance and swing damping settings were performed. Two types of dynamic models, 2-phase and 4-phase models, of the Mauch S-N-S prosthetic knee were developed. The range of the coefficient of determination of the two dynamic models, when compared to the test data, was from 39.9 to 95%. Both dynamic models of this study can be utilized in musculoskeletal modeling studies, to better understand amputee gait and the contributions and interactions of various prosthetic leg components to the ambulatory performance.

Mechanical evaluation of balloon-type gastrostomy devices

Purpose is to evaluate the durability of two commonly used gastrostomy devices. The performance of balloon-type gastrostomy devices was evaluated in an accelerated aging failure mode as well as a feeding tube interlock pullout failure mode. Two commonly used devices were tested: MINI (Applied Medical Technology Inc.) and MIC-Key (Kimberly Clark/Ballard Medical). In the aging test, devices (n = 20) from each manufacturer were pressurized and subjected to controlled pH and temperature conditions to evaluate the product life. In the pullout failure test, devices were subjected to controlled mechanical loading to evaluate the force at which each plastic interlock pulls out of the rubber that encapsulates it. In the aging testing, the MIC-Key devices had a lifespan of 98 ± 34 h and the MINI survived for 1187 ± 422 h. The difference was statistically significant (p < 1 × 10-9). In the pullout testing, the MIC-Key failed at 183 ± 24 N whereas the MINI failed at 202 ± 26 N (p < 0.04). Pullout strength for both devices appears adequate in view of estimated in vivo loads during normal use of the device with the MINI requiring a statistically significantly greater pullout strength. Although the aging tests were performed using an accelerated protocol, the aging tests suggest that the in vivo lifespan and failure mode of the MINI may be superior to the MIC-Key.

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

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

A three-dimensional inverse finite element analysis of the heel pad

Quantification of plantar tissue behavior of the heel pad is essential in developing computational models for predictive analysis of preventive treatment options such as footwear for patients with diabetes. Simulation based studies in the past have generally adopted heel pad properties from the literature, in return using heel-specific geometry with material properties of a different heel. In exceptional cases, patient-specific material characterization was performed with simplified two-dimensional models, without further evaluation of a heel-specific response under different loading conditions. The aim of this study was to conduct an inverse finite element analysis of the heel in order to calculate heel-specific material properties in situ. Multidimensional experimental data available from a previous cadaver study by Erdemir et al. ("An Elaborate Data Set Characterizing the Mechanical Response of the Foot," ASME J. Biomech. Eng., 131(9), pp. 094502) was used for model development, optimization, and evaluation of material properties. A specimen-specific three-dimensional finite element representation was developed. Heel pad material properties were determined using inverse finite element analysis by fitting the model behavior to the experimental data. Compression dominant loading, applied using a spherical indenter, was used for optimization of the material properties. The optimized material properties were evaluated through simulations representative of a combined loading scenario (compression and anterior-posterior shear) with a spherical indenter and also of a compression dominant loading applied using an elevated platform. Optimized heel pad material coefficients were 0.001084 MPa (μ), 9.780 (α) (with an effective Poisson's ratio (ν) of 0.475), for a first-order nearly incompressible Ogden material model. The model predicted structural response of the heel pad was in good agreement for both the optimization (<1.05% maximum tool force, 0.9% maximum tool displacement) and validation cases (6.5% maximum tool force, 15% maximum tool displacement). The inverse analysis successfully predicted the material properties for the given specimen-specific heel pad using the experimental data for the specimen. The modeling framework and results can be used for accurate predictions of the three-dimensional interaction of the heel pad with its surroundings.

Modeling and Optimal Control of an Energy-Storing Prosthetic Knee

Advanced prosthetic knees for transfemoral amputees are currently based on controlled damper mechanisms. Such devices require little energy to operate, but can only produce negative or zero joint power, while normal knee joint function requires alternative phases of positive and negative work. The inability to generate positive work may limit the user’s functional capabilities, may cause undesirable adaptive behavior, and may contribute to excessive metabolic energy cost for locomotion. In order to overcome these problems, we present a novel concept for an energy-storing prosthetic knee, consisting of a rotary hydraulic actuator, two valves, and a spring-loaded hydraulic accumulator. In this paper, performance of the proposed device will be assessed by computational modeling and by simulation of functional activities. A computational model of the hydraulic system was developed, with methods to obtain optimal valve control patterns for any given activity. The objective function for optimal control was based on tracking of joint angles, tracking of joint moments, and the energy cost of operating the valves. Optimal control solutions were obtained, based on data collected from three subjects during walking, running, and a sit-stand-sit cycle. Optimal control simulations showed that the proposed device allows near-normal knee function during all three activities, provided that the accumulator stiffness was tuned to each activity. When the energy storage mechanism was turned off in the simulations, the system functioned as a controlled damper device and optimal control results were similar to literature data on human performance with such devices. When the accumulator stiffness was tuned to walking, simulated performance for the other activities was sub-optimal but still better than with a controlled damper. We conclude that the energy-storing knee concept is valid for the three activities studied, that modeling and optimal control can assist the design process, and that further studies using human subjects are justified.

Simulation of lower limb axial arterial length change during locomotion

The effect of external forces on axial arterial wall mechanics has conventionally been regarded as secondary to hemodynamic influences. However, arteries are similar to muscles in terms of the manner in which they traverse joints, and their three-dimensional geometrical requirements for joint motion. This study considers axial arterial shortening and elongation due to motion of the lower extremity during gait, ascending stairs, and sitting-to-standing motion. Arterial length change was simulated by means of a graphics based anatomic and kinematic model of the lower extremity. This model estimated the axial shortening to be as much as 23% for the femoropopliteal arterial region and as much as 21% for the iliac artery. A strong correlation was observed between femoropopliteal artery shortening and maximum knee flexion angle (r²=0.8) as well as iliac artery shortening and maximum hip angle flexion (r²=0.9). This implies a significant mechanical influence of locomotion on arterial behavior in addition to hemodynamics factors. Vascular tissue has high demands for axial compliance that should be considered in the pathology of atherosclerosis and the design of vascular implants.

Predictive simulation of gait at low gravity reveals skipping as the preferred locomotion strategy

The investigation of gait strategies at low gravity environments gained momentum recently as manned missions to the Moon and to Mars are reconsidered. Although reports by astronauts of the Apollo missions indicate alternative gait strategies might be favored on the Moon, computational simulations and experimental investigations have been almost exclusively limited to the study of either walking or running, the locomotion modes preferred under Earth's gravity. In order to investigate the gait strategies likely to be favored at low gravity a series of predictive, computational simulations of gait are performed using a physiological model of the musculoskeletal system, without assuming any particular type of gait. A computationally efficient optimization strategy is utilized allowing for multiple simulations. The results reveal skipping as more efficient and less fatiguing than walking or running and suggest the existence of a walk-skip rather than a walk-run transition at low gravity. The results are expected to serve as a background to the design of experimental investigations of gait under simulated low gravity.

Estimation of musculotendon kinematics in large musculoskeletal models using multidimensional B-splines

We present a robust and computationally inexpensive method to estimate the lengths and three-dimensional moment arms for a large number of musculotendon actuators of the human lower limb. Using a musculoskeletal model of the lower extremity, a set of values was established for the length of each musculotendon actuator for different lower limb generalized coordinates (joint angles). A multidimensional spline function was then used to fit these data. Muscle moment arms were obtained by differentiating the musculotendon length spline function with respect to the generalized coordinate of interest. This new method was then compared to a previously used polynomial regression method. Compared to the polynomial regression method, the multidimensional spline method produced lower errors for estimating musculotendon lengths and moment arms throughout the whole generalized coordinate workspace. The fitting accuracy was also less affected by the number of dependent degrees of freedom and by the amount of experimental data available. The spline method only required information on musculotendon lengths to estimate both musculotendon lengths and moment arms, thus relaxing data input requirements, whereas the polynomial regression requires different equations to be used for both musculotendon lengths and moment arms. Finally, we used the spline method in conjunction with an electromyography driven musculoskeletal model to estimate muscle forces under different contractile conditions, which showed that the method is suitable for the integration into large scale neuromusculoskeletal models.

Implicit methods for efficient musculoskeletal simulation and optimal control

The ordinary differential equations for musculoskeletal dynamics are often numerically stiff and highly nonlinear. Consequently, simulations require small time steps, and optimal control problems are slow to solve and have poor convergence. In this paper, we present an implicit formulation of musculoskeletal dynamics, which leads to new numerical methods for simulation and optimal control, with the expectation that we can mitigate some of these problems. A first order Rosenbrock method was developed for solving forward dynamic problems using the implicit formulation. It was used to perform real-time dynamic simulation of a complex shoulder arm system with extreme dynamic stiffness. Simulations had an RMS error of only 0.11 degrees in joint angles when running at real-time speed. For optimal control of musculoskeletal systems, a direct collocation method was developed for implicitly formulated models. The method was applied to predict gait with a prosthetic foot and ankle. Solutions were obtained in well under one hour of computation time and demonstrated how patients may adapt their gait to compensate for limitations of a specific prosthetic limb design. The optimal control method was also applied to a state estimation problem in sports biomechanics, where forces during skiing were estimated from noisy and incomplete kinematic data. Using a full musculoskeletal dynamics model for state estimation had the additional advantage that forward dynamic simulations, could be done with the same implicitly formulated model to simulate injuries and perturbation responses. While these methods are powerful and allow solution of previously intractable problems, there are still considerable numerical challenges, especially related to the convergence of gradient-based solvers.

Concurrent musculoskeletal dynamics and finite element analysis predicts altered gait patterns to reduce foot tissue loading

Current computational methods for simulating locomotion have primarily used muscle-driven multibody dynamics, in which neuromuscular control is optimized. Such simulations generally represent joints and soft tissue as simple kinematic or elastic elements for computational efficiency. These assumptions limit application in studies such as ligament injury or osteoarthritis, where local tissue loading must be predicted. Conversely, tissue can be simulated using the finite element method with assumed or measured boundary conditions, but this does not represent the effects of whole body dynamics and neuromuscular control. Coupling the two domains would overcome these limitations and allow prediction of movement strategies guided by tissue stresses. Here we demonstrate this concept in a gait simulation where a musculoskeletal model is coupled to a finite element representation of the foot. Predictive simulations incorporated peak plantar tissue deformation into the objective of the movement optimization, as well as terms to track normative gait data and minimize fatigue. Two optimizations were performed, first without the strain minimization term and second with the term. Convergence to realistic gait patterns was achieved with the second optimization realizing a 44% reduction in peak tissue strain energy density. The study demonstrated that it is possible to alter computationally predicted neuromuscular control to minimize tissue strain while including desired kinematic and muscular behavior. Future work should include experimental validation before application of the methodology to patient care.

Design and Validation of a General Purpose Robotic Testing System for Musculoskeletal Applications

Orthopaedic research on in vitro forces applied to bones, tendons, and ligaments during joint loading has been difficult to perform because of limitations with existing robotic simulators in applying full-physiological loading to the joint under investigation in real time. The objectives of the current work are as follows: (1) describe the design of a musculoskeletal simulator developed to support in vitro testing of cadaveric joint systems, (2) provide component and system-level validation results, and (3) demonstrate the simulator’s usefulness for specific applications of the foot-ankle complex and knee. The musculoskeletal simulator allows researchers to simulate a variety of loading conditions on cadaver joints via motorized actuators that simulate muscle forces while simultaneously contacting the joint with an external load applied by a specialized robot. Multiple foot and knee studies have been completed at the Cleveland Clinic to demonstrate the simulator’s capabilities. Using a variety of general-use components, experiments can be designed to test other musculoskeletal joints as well (e.g., hip, shoulder, facet joints of the spine). The accuracy of the tendon actuators to generate a target force profile during simulated walking was found to be highly variable and dependent on stance position. Repeatability (the ability of the system to generate the same tendon forces when the same experimental conditions are repeated) results showed that repeat forces were within the measurement accuracy of the system. It was determined that synchronization system accuracy was 6.7±2.0 ms and was based on timing measurements from the robot and tendon actuators. The positioning error of the robot ranged from 10 μm to 359 μm, depending on measurement condition (e.g., loaded or unloaded, quasistatic or dynamic motion, centralized movements or extremes of travel, maximum value, or root-mean-square, and x-, y- or z-axis motion). Algorithms and methods for controlling specimen interactions with the robot (with and without muscle forces) to duplicate physiological loading of the joints through iterative pseudo-fuzzy logic and real-time hybrid control are described. Results from the tests of the musculoskeletal simulator have demonstrated that the speed and accuracy of the components, the synchronization timing, the force and position control methods, and the system software can adequately replicate the biomechanics of human motion required to conduct meaningful cadaveric joint investigations.

Optimality principles for model-based prediction of human gait

Although humans have a large repertoire of potential movements, gait patterns tend to be stereotypical and appear to be selected according to optimality principles such as minimal energy. When applied to dynamic musculoskeletal models such optimality principles might be used to predict how a patient's gait adapts to mechanical interventions such as prosthetic devices or surgery. In this paper we study the effects of different performance criteria on predicted gait patterns using a 2D musculoskeletal model. The associated optimal control problem for a family of different cost functions was solved utilizing the direct collocation method. It was found that fatigue-like cost functions produced realistic gait, with stance phase knee flexion, as opposed to energy-related cost functions which avoided knee flexion during the stance phase. We conclude that fatigue minimization may be one of the primary optimality principles governing human gait.

Predictive simulation of gait in rehabilitation

This paper aims at demonstrating the capabilities of predictive, computational simulations of gait in clinical applications. In particular, the gait with bilateral and unilateral Ankle Foot-Orthoses (AFO's) is investigated. The problem is formulated in an optimal control framework where optimal motion and neural excitations to the muscles are computed solely on the basis of an assumed optimality criterion and periodicity constraints. The enormous potential of the approach as well as some of the current limitations are discussed on the light of simulation results.

An elaborate data set characterizing the mechanical response of the foot

Mechanical properties of the foot are responsible for its normal function and play a role in various clinical problems. Specifically, we are interested in quantification of foot mechanical properties to assist the development of computational models for movement analysis and detailed simulations of tissue deformation. Current available data are specific to a foot region and the loading scenarios are limited to a single direction. A data set that incorporates regional response, to quantify individual function of foot components, as well as the overall response, to illustrate their combined operation, does not exist. Furthermore, the combined three-dimensional loading scenarios while measuring the complete three-dimensional deformation response are lacking. When combined with an anatomical image data set, development of anatomically realistic and mechanically validated models becomes possible. Therefore, the goal of this study was to record and disseminate the mechanical response of a foot specimen, supported by imaging data. Robotic testing was conducted at the rear foot, forefoot, metatarsal heads, and the foot as a whole. Complex foot deformations were induced by single mode loading, e.g., compression, and combined loading, e.g., compression and shear. Small and large indenters were used for heel and metatarsal head loading, an elevated platform was utilized to isolate the rear foot and forefoot, and a full platform compressed the whole foot. Three-dimensional tool movements and reaction loads were recorded simultaneously. Computed tomography scans of the same specimen were collected for anatomical reconstruction a priori. The three-dimensional mechanical response of the specimen was nonlinear and viscoelastic. A low stiffness region was observed starting with contact between the tool and foot regions, increasing with loading. Loading and unloading responses portrayed hysteresis. Loading range ensured capturing the toe and linear regions of the load deformation curves for the dominant loading direction, with the rates approximating those of walking. A large data set was successfully obtained to characterize the overall and the regional mechanical responses of an intact foot specimen under single and combined loads. Medical imaging complemented the mechanical testing data to establish the potential relationship between the anatomical architecture and mechanical responses and to further develop foot models that are mechanically realistic and anatomically consistent. This combined data set has been documented and disseminated in the public domain to promote future development in foot biomechanics.

Gender dimorphic ACL strain in response to combined dynamic 3D knee joint loading: implications for ACL injury risk

While gender-based differences in knee joint anatomies/laxities are well documented, the potential for them to precipitate gender-dimorphic ACL loading and resultant injury risk has not been considered. To this end, we generated gender-specific models of ACL strain as a function of any six degrees of freedom (6DOF) knee joint load state via a combined cadaveric and analytical approach. Continuously varying joint forces and torques were applied to five male and five female cadaveric specimens and recorded along with synchronous knee flexion and ACL strain data. All data (approximately 10,000 samples) were submitted to specimen-specific regression analyses, affording ACL strain predictions as a function of the combined 6 DOF knee loads. Following individual model verifications, generalized gender-specific models were generated and subjected to 6 DOF external load scenarios consistent with both a clinical examination and a dynamic sports maneuver. The ensuing model-based strain predictions were subsequently examined for gender-based discrepancies. Male and female specimen-specific models predicted ACL strain within 0.51%+/-0.10% and 0.52%+/-0.07% of the measured data respectively, and explained more than 75% of the associated variance in each case. Predicted female ACL strains were also significantly larger than respective male values for both simulated 6 DOF load scenarios. Outcomes suggest that the female ACL will rupture in response to comparatively smaller external load applications. Future work must address the underlying anatomical/laxity contributions to knee joint mechanical and resultant ACL loading, ultimately affording prevention strategies that may cater to individual joint vulnerabilities.

Adaptive surrogate modeling for efficient coupling of musculoskeletal control and tissue deformation models

Finite element (FE) modeling and multibody dynamics have traditionally been applied separately to the domains of tissue mechanics and musculoskeletal movements, respectively. Simultaneous simulation of both domains is needed when interactions between tissue and movement are of interest, but this has remained largely impractical due to the high computational cost. Here we present a method for the concurrent simulation of tissue and movement, in which state of the art methods are used in each domain, and communication occurs via a surrogate modeling system based on locally weighted regression. The surrogate model only performs FE simulations when regression from previous results is not within a user-specified tolerance. For proof of concept and to illustrate feasibility, the methods were demonstrated on an optimization of jumping movement using a planar musculoskeletal model coupled to a FE model of the foot. To test the relative accuracy of the surrogate model outputs against those of the FE model, a single forward dynamics simulation was performed with FE calls at every integration step and compared with a corresponding simulation with the surrogate model included. Neural excitations obtained from the jump height optimization were used for this purpose and root mean square (RMS) difference between surrogate and FE model outputs (ankle force and moment, peak contact pressure and peak von Mises stress) were calculated. Optimization of the jump height required 1800 iterations of the movement simulation, each requiring thousands of time steps. The surrogate modeling system only used the FE model in 5% of time steps, i.e., a 95% reduction in computation time. Errors introduced by the surrogate model were less than 1 mm in jump height and RMS errors of less than 2 N in ground reaction force, 0.25 Nm in ankle moment, and 10 kPa in peak tissue stress. Adaptive surrogate modeling based on local regression allows efficient concurrent simulations of tissue mechanics and musculoskeletal movement.

Investigating isolated neuromuscular control contributions to non-contact anterior cruciate ligament injury risk via computer simulation methods

BACKGROUND

Despite the ongoing evolution of anterior cruciate ligament injury prevention methods, injury rates and the associated sex-disparity remain. Strategies capable of successfully countering key control parameters existent within the injury mechanism thus remain elusive. Forward dynamics model simulations afford an expedited means to study realistic injury causing scenarios, while controlling all facets of the movement control strategy. Utilizing these methods, the current study examined the potential for perturbations in key initial contact neuromuscular parameters to injure the anterior cruciate ligament during the stance phase of sidestep cutting maneuvers.

METHODS

Controlled experiments were performed on optimized and validated subject-specific forward dynamic musculoskeletal sidestep models generated from 10 male and 10 female data sets. Random perturbations (n=5000) were applied to initial contact kinematic and muscle activation parameters in these baseline models and then to those with prescribed systematic modifications in initial hip and knee flexion, hip internal rotation and hip internal rotation velocity postures. The number of injuries via an isolated anterior tibial shear (>2000 N) or knee valgus load (>125 Nm) mechanism was determined for each of the seven model conditions and subsequently compared.

FINDINGS

Neuromuscular control perturbations produced peak stance phase (0-100 ms) knee valgus loads large enough to induce anterior cruciate ligament injury. Decreases and increases in combined initial contact hip and knee flexion postures and hip internal rotation velocity produced significant increases and decreases in these valgus-induced ACL injury rates respectively.

INTERPRETATION

Anterior cruciate ligament injury via a valgus load mechanism is more likely during sidestepping when landing in a more extended posture, or with increased hip external rotation velocity. The fact that injury rates are reduced when these control parameters are reversed suggests they should be central to ongoing prevention strategy developments.