Load sharing among spinal elements of a motion segment in extension and lateral bending

A linear optimization model was formulated using a semi-experimental protocol to estimate the forces in the spinal elements of a lumbar motion segment subjected to an extension or lateral bending moment with and without a 120 N compressive preload. A morphometer was used to acquire the three-dimensional locations of the disk center, facet centers and ligament origin and insertion sites with the specimen in a "neutral" position. The relative motion of the superior vertebra, under the loading conditions tested, was monitored using a Selspot II system. These data allowed the formulation of the static equilibrium equations for the superior vertebra at each of the loading conditions mentioned above. A linear optimization technique was used, along with a suitable cost function, to find an optimum solution for the set of equations and imposed constraints. Results showed that for 6.9 Nm of extension moment, each facet carried a load of 52 N, with the disk carrying an axial tensile load of 104 N. At the 6.9 Nm extension moment coupled with 120 N preload, each facet carried a load of 77.2 N and the disk an axial tensile load of 37 N. In right lateral bending, with and without preload, the load was distributed among the right facet, the disk, the left ligamentum flavum and the left capsular ligament. At the 6.9 Nm load step without preload the right facet carried an axial load of 127.01 N with the disk carrying an axial compressive load of 7.8 N. Ligament forces for this step for the left ligamentum flavum and capsular ligament, respectively, were 61.03 N and 65.14 N. The addition of 120 N of preload reduced the load on the right facet to 83.5 N. The compressive load in the disk increased to 107.5 N. The corresponding ligament forces were 43.2 N (left ligamentum flavum) and 50.7 N (left capsular ligament).     

Estimating total knee replacement joint load ratios from kinematics

Accurate prediction of loads acting at the joint in total knee replacement (TKR) patients is key to developing experimental or computational simulations which evaluate implant designs under physiological loading conditions. In vivo joint loads have been measured for a small number of telemetric TKR patients, but in order to assess device performance across the entire patient population, a larger patient cohort is necessary. This study investigates the accuracy of predicting joint loads from joint kinematics. Specifically, the objective of the study was to assess the accuracy of internal–external (I–E) and anterior–posterior (A–P) joint load predictions from I–E and A–P motions under a given compressive load, and to evaluate the repeatability of joint load ratios (I–E torque to compressive force (I–E:C), and A–P force to compressive force (A–P:C)) for a range of compressive loading profiles. A tibiofemoral finite element model was developed and used to simulate deep knee bend, chair-rise and step-up activities for five patients. Root-mean-square (RMS) differences in I–E:C and A–P:C load ratios between telemetric measurements and model predictions were less than 1.10e–3 Nm/N and 0.035 N/N for all activities. I–E:C and A–P:C load ratios were consistently reproduced regardless of the compressive force profile applied (RMS differences less than 0.53e–3 Nm/N and 0.010 N/N, respectively). When error in kinematic measurement was introduced to the model, joint load predictions were forgiving to kinematic measurement error when conformity between femoral and tibial components was low. The prevalence of kinematic data, in conjunction with the analysis presented here, facilitates determining the scope of A–P and I–E joint loading ratios experienced by the TKR population.     

Influence of loading rate and cable migration on fiberoptic measurement of tendon force

Several investigators have recently used fiberoptic cables to measure tendon forces in situ. The technique may be subject to significant error due to cable migration and differences in the loading rates used for calibration and those experienced during measurement. This in vitro study examined the impact of these potential sources of error on transducer accuracy. A fiberoptic cable was passed perpendicular to the fibers of four Achilles tendons in the mediolateral direction and each specimen was cyclically loaded to 1000 N. The influence of loading rate on transducer output was investigated by comparing results from tests conducted at 20, 200 and 1000 N/s. The effect of cable migration was examined by comparing the outputs obtained after displacing the cable one tendon width medially and laterally along its path in the tendon and then repeating the 200 N/s testing protocol. It was possible to obtain nonlinear specimen-specific relationships between the fiberoptic output and tendon force. Differences in loading rate resulted in root-mean-square (RMS) errors not larger than 17% maximum load. Hysteresis effects caused RMS errors smaller than 5% maximum load. Cable migration errors were less than 27%. The total RMS error due to the combined effects of loading rate difference and cable movement was less than 32%. Fiberoptic measurement of tendon force is attractive due to its low cost, easy implementation and comparable accuracy relative to other implantable force transducers. Although additional factors such as cable placement, edge artifacts due where the transducer exits the skin and non-uniform loading may also influence fiberoptic output, careful control of loading rate and transducer movement during calibration is imperative if maximum accuracy is to be achieved.     

Predicting muscle forces in gait from EMG signals and musculotendon kinematics

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

The interaction of muscle moment arm,knee laxity,and torque in a multi-scale musculoskeletal model of the lower limb

IntroductionMusculoskeletal modeling allows insight into the interaction of muscle force and knee joint kinematics that cannot be measured in the laboratory. However, musculoskeletal models of the lower extremity commonly use simplified representations of the knee that may limit analyses of the interaction between muscle forces and joint kinematics. The goal of this research was to demonstrate how muscle forces alter knee kinematics and consequently muscle moment arms and joint torque in a musculoskeletal model of the lower limb that includes a deformable representation of the knee.MethodsTwo musculoskeletal models of the lower limb including specimen-specific articular geometries and ligament deformability at the knee were built in a finite element framework and calibrated to match mean isometric torque data collected from 12 healthy subjects. Muscle moment arms were compared between simulations of passive knee flexion and maximum isometric knee extension and flexion. In addition, isometric torque results were compared with predictions using simplified knee models in which the deformability of the knee was removed and the kinematics at the joint were prescribed for all degrees of freedom.ResultsPeak isometric torque estimated with a deformable knee representation occurred between 45° and 60° in extension, and 45° in flexion. The maximum isometric flexion torques generated by the models with deformable ligaments were 14.6% and 17.9% larger than those generated by the models with prescribed kinematics; by contrast, the maximum isometric extension torques generated by the models were similar. The change in hamstrings moment arms during isometric flexion was greater than that of the quadriceps during isometric extension (a mean RMS difference of 9.8 mm compared to 2.9 mm, respectively).DiscussionThe large changes in the moment arms of the hamstrings, when activated in a model with deformable ligaments, resulted in changes to flexion torque. When simulating human motion, the inclusion of a deformable joint in a multi-scale musculoskeletal finite element model of the lower limb may preserve the realistic interaction of muscle force with knee kinematics and torque.     

A validated three-dimensional computational model of a human knee joint

This paper presents a three-dimensional finite element tibio-femoral joint model of a human knee that was validated using experimental data. The geometry of the joint model was obtained from magnetic resonance (MR) images of a cadaveric knee specimen. The same specimen was biomechanically tested using a robotic/universal force-moment sensor (UFS) system and knee kinematic data under anterior-posterior tibial loads (up to 100 N) were obtained. In the finite element model (FEM), cartilage was modeled as an elastic material, ligaments were represented as nonlinear elastic springs, and menisci were simulated by equivalent-resistance springs. Reference lengths (zero-load lengths) of the ligaments and stiffness of the meniscus springs were estimated using an optimization procedure that involved the minimization of the differences between the kinematics predicted by the model and those obtained experimentally. The joint kinematics and in-situ forces in the ligaments in response to axial tibial moments of up to 10 Nm were calculated using the model and were compared with published experimental data on knee specimens. It was also demonstrated that the equivalent-resistance springs representing the menisci are important for accurate calculation of knee kinematics. Thus, the methodology developed in this study can be a valuable tool for further analysis of knee joint function and could serve as a step toward the development of more advanced computational knee models.     

Identification of passive elastic joint moment-angle relationships in the lower extremity

The purpose of this study was to develop a method for identifying subject-specific passive elastic joint moment-angle relationships in the lower extremity, which could subsequently be used to estimate passive contributions to joint kinetics during gait. Twenty healthy young adults participated in the study. Subjects were positioned side-lying with their dominant limb supported on a table via low-friction carts. A physical therapist slowly manipulated the limb through full sagittal hip, knee, and ankle ranges of motion using two hand-held 3D load cells. Lower extremity kinematics, measured with a passive marker motion capture system, and load cell readings were used to compute joint angles and associated passive joint moments. We formulated a passive joint moment-angle model that included eight exponential functions to account for forces generated via the passive stretch of uni-articular structures and bi-articular muscles. Model parameters were estimated for individual subjects by minimizing the sum of squared errors between model predicted and experimentally measured moments. The model predictions closely replicated measured joint moments with average root-mean-squared errors of 2.5, 1.4, and 0.7 Nm about the hip, knee, and ankle respectively. We show that the models can be coupled with gait kinematics to estimate passive joint moments during walking. Passive hip moments were substantial from terminal stance through initial swing, with energy being stored as the hip extended and subsequently returned during pre- and initial swing. We conclude that the proposed methodology could provide quantitative insights into the potentially important role that passive mechanisms play in both normal and abnormal gait.     

An analytical approach to determine the in situ forces in the glenohumeral ligaments.

The purpose of this study was to use an analytical approach to determine the forces in the glenohumeral ligaments during joint motion. Predictions from the analytical approach were validated by comparing them to experimental data. Using a geometric model, the lengths of the four glenohumeral ligaments were determined during anterior-posterior loading simulations and forward flexion-extension. The corresponding force in each structure was subsequently calculated based on length data via load-elongation curves obtained experimentally. During the anterior loading simulation at 0 deg of abduction, the superior glenohumeral ligament carried up to 71 N at the maximally translated position. At 90 deg of abduction, the anterior band of the inferior glenohumeral ligament had the highest force of 45 N during anterior loading. These results correlated well with those found in previous experimental studies. We believe that this validated analytical approach can be used to predict the forces in the glenohumeral ligaments during more complex joint motion as well as assist surgeons during shoulder repair procedures.     

Combined measurement and modeling of specimen-specific knee mechanics for healthy and ACL-deficient conditions

Quantifying the mechanical environment at the knee is crucial for developing successful rehabilitation and surgical protocols. Computational models have been developed to complement in vitro studies, but are typically created to represent healthy conditions, and may not be useful in modeling pathology and repair. Thus, the objective of this study was to create finite element (FE) models of the natural knee, including specimen-specific tibiofemoral (TF) and patellofemoral (PF) soft tissue structures, and to evaluate joint mechanics in intact and ACL-deficient conditions. Simulated gait in a whole joint knee simulator was performed on two cadaveric specimens in an intact state and subsequently repeated following ACL resection. Simulated gait was performed using motor-actuated quadriceps, and loads at the hip and ankle. Specimen-specific FE models of these experiments were developed in both intact and ACL-deficient states. Model simulations compared kinematics and loading of the experimental TF and PF joints, with average RMS differences [max] of 3.0° [8.2°] and 2.1° [8.4°] in rotations, and 1.7 [3.0] and 2.5 [5.1] mm in translations, for intact and ACL-deficient states, respectively. The timing of peak quadriceps force during stance and swing phase of gait was accurately replicated within 2° of knee flexion and with an average error of 16.7% across specimens and pathology. Ligament recruitment patterns were unique in each specimen; recruitment variability was likely influenced by variations in ligament attachment locations. ACL resections demonstrated contrasting joint mechanics in the two specimens with altered knee motion shown in one specimen (up to 5 mm anterior tibial translation) while increased TF joint loading was shown in the other (up to 400 N).     

Development and validation of a 3-D model to predict knee joint loading during dynamic movement

The purpose of this study was to develop a subject-specific 3-D model of the lower extremity to predict neuromuscular control effects on 3-D knee joint loading during movements that can potentially cause injury to the anterior cruciate ligament (ACL) in the knee. The simulation consisted of a forward dynamic 3-D musculoskeletal model of the lower extremity, scaled to represent a specific subject. Inputs of the model were the initial position and velocity of the skeletal elements, and the muscle stimulation patterns. Outputs of the model were movement and ground reaction forces, as well as resultant 3-D forces and moments acting across the knee joint. An optimization method was established to find muscle stimulation patterns that best reproduced the subject's movement and ground reaction forces during a sidestepping task. The optimized model produced movements and forces that were generally within one standard deviation of the measured subject data. Resultant knee joint loading variables extracted from the optimized model were comparable to those reported in the literature. The ability of the model to successfully predict the subject's response to altered initial conditions was quantified and found acceptable for use of the model to investigate the effect of altered neuromuscular control on knee joint loading during sidestepping. Monte Carlo simulations (N = 100,000) using randomly perturbed initial kinematic conditions, based on the subject's variability, resulted in peak anterior force, valgus torque and internal torque values of 378 N, 94 Nm and 71 Nm, respectively, large enough to cause ACL rupture. We conclude that the procedures described in this paper were successful in creating valid simulations of normal movement, and in simulating injuries that are caused by perturbed neuromuscular control.     

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

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

Inter-insertional distance is a poor correlate for ligament load: Analysis from in vivo gait kinetics data

In many analytic models of the knee joint, inter-insertional distance is used as the measure to define the load in a ligament. In addition, the direction of the load is taken to be the direction between the two insertions. Our in vivo data on the ovine ligament loads during gait, however, indicate that a wide range of forces is possible in the ligament for any specified inter-insertional distance. To understand the complex relationship between the bone orientations and ligament load better, an artificial neural network (ANN) model was developed. The six degree-of-freedom (6-DOF) in vivo kinematics of femur relative to tibia (joint kinematics) was used as input, and the magnitude of the anterior cruciate ligament (ACL) load was used as output/target. While the trained network was able to predict peak ligament loads with remarkable accuracy (R-square=0.98), an explicit relationship between joint kinematics and ACL load could not be determined. To examine the experimental and ANN observations further, a finite element (FE) model of the ACL was created. The geometry of the FE model was reconstructed from magnetic resonance images (MRI) of an ACL, and an isotropic, hyperelastic, nearly incompressible constitutive model was implemented for the ACL. The FE simulation results also indicate that a range of loads is possible in the ACL for a given inter-insertional distance, in concordance with the experimental/ANN observations. This study provides new insights for models of the knee joint; a simple force–length relationship for the ligament is not exact, nor is a single point to single point direction. More detailed microstructure-function data is required.     

A computationally efficient strategy to estimate muscle forces in a finite element musculoskeletal model of the lower limb

Concurrent multiscale simulation strategies are required in computational biomechanics to study the interdependence between body scales. However, detailed finite element models rarely include muscle recruitment due to the computational burden of both the finite element method and the optimization strategies widely used to estimate muscle forces. The aim of this study was twofold: first, to develop a computationally efficient muscle force prediction strategy based on proportional-integral-derivative (PID) controllers to track gait and chair rise experimental joint motion with a finite element musculoskeletal model of the lower limb, including a deformable knee representation with 12 degrees of freedom; and, second, to demonstrate that the inclusion of joint-level deformability affects muscle force estimation by using two different knee models and comparing muscle forces between the two solutions. The PID control strategy tracked experimental hip, knee, and ankle flexion/extension with root mean square errors below 1°, and estimated muscle, contact and ligament forces in good agreement with previous results and electromyography signals. Differences up to 11% and 20% in the vasti and biceps femoris forces, respectively, were observed between the two knee models, which might be attributed to a combination of differing joint contact geometry, ligament behavior, joint kinematics, and muscle moment arms. The tracking strategy developed in this study addressed the inevitable tradeoff between computational cost and model detail in musculoskeletal simulations and can be used with finite element musculoskeletal models to efficiently estimate the interdependence between muscle forces and tissue deformation.     

Suture augmentation following ACL injury to restore the function of the ACL, MCL, and medial meniscus in the goat stifle joint

Functional tissue engineering (FTE) approaches have shown promise in healing an injured anterior cruciate ligament (ACL) of the knee. Nevertheless, additional mechanical augmentation is needed to maintain joint stability and appropriate loading of the joint while the ACL heals. The objective of this study was to quantitatively evaluate how mechanical augmentation using sutures restores the joint kinematics as well as the distribution of loading among the ACL, medial collateral ligament, and medial meniscus (MM) in response to externally applied loads. Eight goat stifle joints were tested on a robotic/universal force-moment sensor testing system under two loading conditions: (1) a 67N anterior tibial load (ATL) and (2) a 67N ATL with 100N axial compression. For each joint, four experimental conditions were tested at 30°, 60°, and 90° of flexion: the (1) intact and (2) ACL-deficient joint, as well as following (3) suture repair of the transected ACL, and (4) augmentation using sutures passed from the femur to the tibia. Under the 67N ATL, suture augmentation could restore the anterior tibial translation (ATT) to within 3mm of the intact joint (p>0.05), representing a 54-76% improvement over suture repair (p<0.05). With the additional axial compression, the ATT and in-situ forces of the sutures following suture augmentation remained 2-3 times closer to normal (p<0.05). Also, the in-situ forces in the MM were 58-73% lower (p<0.05). Thus, suture augmentation may be helpful in combination with FTE approaches for ACL healing by providing the needed initial joint stability while lowering the loads on the MM.     

Mechanical properties of the avian acrocoracohumeral ligament and its role in shoulder stabilization in flight

Control of movement in the avian shoulder joint is fundamental to understanding the avian wingstroke. The acrocoracohumeral ligament (AHL) is thought to play a key role in stabilizing the glenoid and balancing the pectoralis in gliding flight. If the AHL has to be taut to balance the pectoralis, then it must constrain glenohumeral motion during flapping flight as well. However, birds vary wing kinematics depending on flight speed and behavior. How can a passive ligament accommodate such varying joint movements? Herein, mechanical testing and 3-D modeling are used to link the mechanical properties and morphology of the AHL to its functional role during flapping flight. The bone-ligament-bone complex of the pigeon (Columba livia) fails at a tensile loading of 141 ± 18 N (± s .D., n = 10) or 39 times body weight, which corresponds to a failure stress of 51 MPa, well above expected loads during flight. Simulated AHL length changes, comparisons to glenohumeral kinematics from the literature, and manipulations of partially dissected pigeon specimens all support the hypothesis that the AHL remains taut through downstroke and most of upstroke while becoming slack during the downstroke/upstroke transition. The digital AHL model provides a mechanism for explaining how the AHL can stabilize the shoulder joint under a broad array of humeral paths by constraining the coordination of glenohumeral degrees of freedom.     

Quantifying in vivo laxity in the anterior cruciate ligament and individual knee joint structures

A custom knee loading apparatus (KLA), when used in conjunction with magnetic resonance imaging, enables in vivo measurement of the gross anterior laxity of the knee joint. A numerical model was applied to the KLA to understand the contribution of the individual joint structures and to estimate the stiffness of the anterior-cruciate ligament (ACL). The model was evaluated with a cadaveric study using an in situ knee loading apparatus and an ElectroForce test system. A constrained optimization solution technique was able to predict the restraining forces within the soft-tissue structures and joint contact. The numerical model presented here allowed in vivo prediction of the material stiffness parameters of the ACL in response to applied anterior loading. Promising results were obtained for in vivo load sharing within the structures. The numerical model overestimated the ACL forces by 27.61–92.71%. This study presents a novel approach to estimate ligament stiffness and provides the basis to develop a robust and accurate measure of in vivo knee joint laxity.     

A 3D lower limb musculoskeletal model for simultaneous estimation of musculo-tendon,joint contact,ligament and bone forces during gait

Musculo-tendon forces and joint reaction forces are typically estimated using a two-step method, computing first the musculo-tendon forces by a static optimization procedure and then deducing the joint reaction forces from the force equilibrium. However, this method does not allow studying the interactions between musculo-tendon forces and joint reaction forces in establishing this equilibrium and the joint reaction forces are usually overestimated. This study introduces a new 3D lower limb musculoskeletal model based on a one-step static optimization procedure allowing simultaneous musculo-tendon, joint contact, ligament and bone forces estimation during gait. It is postulated that this approach, by giving access to the forces transmitted by these musculoskeletal structures at hip, tibiofemoral, patellofemoral and ankle joints, modeled using anatomically consistent kinematic models, should ease the validation of the model using joint contact forces measured with instrumented prostheses. A blinded validation based on four datasets was made under two different minimization conditions (i.e., C1 – only musculo-tendon forces are minimized, and C2 – musculo-tendon, joint contact, ligament and bone forces are minimized while focusing more specifically on tibiofemoral joint contacts). The results show that the model is able to estimate in most cases the correct timing of musculo-tendon forces during normal gait (i.e., the mean coefficient of active/inactive state concordance between estimated musculo-tendon force and measured EMG envelopes was C1: 65.87% and C2: 60.46%). The results also showed that the model is potentially able to well estimate joint contact, ligament and bone forces and more specifically medial (i.e., the mean RMSE between estimated joint contact force and in vivo measurement was C1: 1.14BW and C2: 0.39BW) and lateral (i.e., C1: 0.65BW and C2: 0.28BW) tibiofemoral contact forces during normal gait. However, the results remain highly influenced by the optimization weights that can bring to somewhat aphysiological musculo-tendon forces.     

Computational biodynamics of human knee joint in gait: from muscle forces to cartilage stresses

Using a validated finite element model of the intact knee joint we aim to compute muscle forces and joint response in the stance phase of gait. The model is driven by reported in vivo kinematics-kinetics data and ground reaction forces in asymptomatic subjects. Cartilage layers and menisci are simulated as depth-dependent tissues with collagen fibril networks. A simplified model with less refined mesh and isotropic depth-independent cartilage is also considered to investigate the effect of model accuracy on results. Muscle forces and joint detailed response are computed following an iterative procedure yielding results that satisfy kinematics/kinetics constraints while accounting at deformed configurations for muscle forces and passive properties. Predictions confirm that muscle forces and joint response alter substantially during the stance phase and that a simplified joint model may accurately be used to estimate muscle forces but not necessarily contact forces/areas, tissue stresses/strains, and ligament forces. Predictions are in general agreement with results of earlier studies. Performing the analyses at 6 periods from beginning to the end (0%, 5%, 25%, 50%, 75% and 100%), hamstrings forces peaked at 5%, quadriceps forces at 25% whereas gastrocnemius forces at 75%. ACL Force reached its maximum of 343 N at 25% and decreased thereafter. Contact forces reached maximum at 5%, 25% and 75% periods with the medial compartment carrying a major portion of load and experiencing larger relative movements and cartilage strains. Much smaller contact stresses were computed at the patellofemoral joint. This novel iterative kinematics-driven model is promising for the joint analysis in altered conditions.     

A new method to investigate in vivo knee behavior using a finite element model of the lower limb

Several finite element models have been developed for estimating the mechanical response of joint internal structures, where direct or indirect in vivo measurement is difficult or impossible. The quality of the predictions made by those models is largely dependent on the quality of the experimental data (e.g. load/displacement) used to drive them. Also numerical problems have been described in the literature when using implicit finite element techniques to simulate problems that involve contacts and large displacements. In this study, a unique strategy was developed combining high accuracy in vivo three-dimensional kinematics and a lower limb finite element model based on explicit finite element techniques. The method presents an analytical technique applied to a dynamic loading condition (impact during hopping on one leg). The validation of the lower limb model focused on the response of the whole model and the knee joint in particular to the imposed 3D femoral in vivo kinematics and ground reaction forces. The approach outlined in this study introduces a generic tool for the study of in vivo knee joint behavior.     

Estimating the L5S1 flexion/extension moment in symmetrical lifting using a simplified ambulatory measurement system

Mechanical loading of the spine has been shown to be an important risk factor for the development of low-back pain. Inertial motion capture (IMC) systems might allow measuring lumbar moments in realistic working conditions, and thus support evaluation of measures to reduce mechanical loading. As the number of sensors limits applicability, the objective of this study was to investigate the effect of the number of sensors on estimates of L5S1 moments.Hand forces, ground reaction forces (GRF) and full-body kinematics were measured using a gold standard (GS) laboratory setup. In the ambulatory setup, hand forces were estimated based on the force plates measured GRF and body kinematics that were measured using (subsets of) an IMC system. Using top-down inverse dynamics, L5S1 flexion/extension moments were calculated.RMSerrors (Nm) were lowest (16.6) with the full set of 17 sensors and increased to 20.5, 22 and 30.6, for 8, 6 and 4 sensors. Absolute errors in peak moments (Nm) ranged from 17.7 to 16.4, 16.9 and 49.3 Nm, for IMC setup’s with 17, 8, 6 and 4 sensors, respectively. When horizontal GRF were neglected for 6 sensors, RMSerrors and peak moment errors decreased from 22 to 17.3 and from 16.9 to 13 Nm, respectively.In conclusion, while reasonable moment estimates can be obtained with 6 sensors, omitting the forearm sensors led to unacceptable errors. Furthermore, vertical GRF information is sufficient to estimate L5S1 moments in lifting.