Muscle force estimation in clinical gait analysis using AnyBody and OpenSim

A variety of musculoskeletal models are applied in different modelling environments for estimating muscle forces during gait. Influence of different modelling assumptions and approaches on model outputs are still not fully understood, while direct comparisons of standard approaches have been rarely undertaken. This study seeks to compare joint kinematics, joint kinetics and estimated muscle forces of two standard approaches offered in two different modelling environments (AnyBody, OpenSim). It is hypothesised that distinctive differences exist for individual muscles, while summing up synergists show general agreement. Experimental data of 10 healthy participants (28 ± 5 years, 1.72 ± 0.08 m, 69 ± 12 kg) was used for a standard static optimisation muscle force estimation routine in AnyBody and OpenSim while using two gait-specific musculoskeletal models. Statistical parameter mapping paired t-test was used to compare joint angle, moment and muscle force waveforms in Matlab. Results showed differences especially in sagittal ankle and hip angles as well as sagittal knee moments. Differences were also found for some of the muscles, especially of the triceps surae group and the biceps femoris short head, which occur as a result of different anthropometric and anatomical definitions (mass and inertia of segments, muscle properties) and scaling procedures (static vs. dynamic). Understanding these differences and their cause is crucial to operate such modelling environments in a clinical setting. Future research should focus on alternatives to classical generic musculoskeletal models (e.g. implementation of functional calibration tasks), while using experimental data reflecting normal and pathological gait to gain a better understanding of variations and divergent behaviour between approaches.     

Biomechanics of the rabbit knee and ankle: muscle, ligament, and joint contact force predictions

Mathematical models of small animals that predict in vivo forces acting on the lower extremities are critical for studies of musculoskeletal biomechanics and diseases. Rabbits are advantageous in this regard because they remodel their cortical bone similar to humans. Here, we enhance a recent mathematical model of the rabbit knee joint to include the loading behavior of individual muscles, ligaments, and joint contact at the knee and ankle during the stance phase of hopping. Geometric data from the hindlimbs of three adult New Zealand white rabbits, combined with previously reported intersegmental forces and moments, were used as inputs to the model. Muscle, ligament, and joint contact forces were computed using optimization techniques assuming that muscle endurance is maximized and ligament strain energy resists tibial shear force along an inclined plateau. Peak forces developed by the quadriceps and gastrocnemius muscle groups and by compressive knee contact were within the range of theoretical and in vivo predictions. Although a minimal force was carried by the anterior cruciate and medial collateral ligaments, force patterns in the posterior cruciate ligament were consistent with in vivo tibial displacement patterns during hopping in rabbits. Overall, our predictions compare favorably with theoretical estimates and in vivo measurements in rabbits, and enhance previous models by providing individual muscle, ligament, and joint contact information to predict in vivo forces acting on the lower extremities in rabbits.     

Individual muscle contributions to the axial knee joint contact force during normal walking

Muscles are significant contributors to the high joint forces developed in the knee during human walking. Not only do muscles contribute to the knee joint forces by acting to compress the joint, but they also develop joint forces indirectly through their contributions to the ground reaction forces via dynamic coupling. Thus, muscles can have significant contributions to forces at joints they do not span. However, few studies have investigated how the major lower-limb muscles contribute to the knee joint contact forces during walking. The goal of this study was to use a muscle-actuated forward dynamics simulation of walking to identify how individual muscles contribute to the axial tibio-femoral joint force. The simulation results showed that the vastii muscles are the primary contributors to the axial joint force in early stance while the gastrocnemius is the primary contributor in late stance. The tibio-femoral joint force generated by these muscles was at times greater than the muscle forces themselves. Muscles that do not cross the knee joint (e.g., the gluteus maximus and soleus) also have significant contributions to the tibio-femoral joint force through their contributions to the ground reaction forces. Further, small changes in walking kinematics (e.g., knee flexion angle) can have a significant effect on the magnitude of the knee joint forces. Thus, altering walking mechanics and muscle coordination patterns to utilize muscle groups that perform the same biomechanical function, yet contribute less to the knee joint forces may be an effective way to reduce knee joint loading during walking.     

A task-specific validation of homogeneous non-linear optimisation approaches

In biomechanics, musculoskeletal models are typically redundant. This situation is referred to as the distribution problem. Often, static, non-linear optimisation methods of the form “min: φ(f) subject to mechanical and muscular constraints” have been used to extract a unique set of muscle forces. Here, we present a method for validating this class of non-linear optimisation approaches where the homogeneous cost function, φ(f), is used to solve the distribution problem. We show that the predicted muscle forces for different loading conditions are scaled versions of each other if the joint loading conditions are just scaled versions. Therefore, we can calculate the theoretical muscle forces for different experimental conditions based on the measured muscle forces and joint loadings taken from one experimental condition and assuming that all input into the optimisation (e.g., moment arms, muscle attachment sites, size, fibre type distribution) and the optimisation approach are perfectly correct. Thus predictions of muscle force for other experimental conditions are accurate if the optimisation approach is appropriate, independent of the musculoskeletal geometry and other input required for the optimisation procedure. By comparing the muscle forces predicted in this way to the actual muscle forces obtained experimentally, we conclude that convex homogeneous non-linear optimisation approaches cannot predict individual muscle forces properly, as force-sharing among synergistic muscles obtained experimentally are not just scaled versions of joint loading, not even in a first approximation.     

Can a reduction approach predict reliable joint contact and musculo-tendon forces?

Musculoskeletal models generally solve the muscular redundancy by numerical optimisation. They have been extensively validated using instrumented implants. Conversely, a reduction approach considers only one flexor or extensor muscle group at the time to equilibrate the inter-segmental joint moment. It is not clear if such models can still predict reliable joint contact and musculo-tendon forces during gait.Tibiofemoral contact force and gastrocnemii, quadriceps, and hamstrings musculo-tendon forces were estimated using a reduction approach for five subjects walking with an instrumented prosthesis. The errors in the proximal-distal tibiofemoral contact force fell in the range (0.3–0.9 body weight) reported in the literature for musculoskeletal models using numerical optimisation. The musculo-tendon forces were in agreement with the EMG envelops and appeared comparable to the ones reported in the literature with generic musculoskeletal models.Although evident simplifications and limitations, it seems that the reduction approach can provided quite reliable results. It can be a useful pedagogical tool in biomechanics, e.g. to illustrate the theoretical differences between inter-segmental and contact forces, and can provide a first estimate of the joint loadings in subjects with limited musculoskeletal deformities and neurological disorders.     

Critical analysis of musculoskeletal modelling complexity in multibody biomechanical models of the upper limb

The inverse dynamics technique applied to musculoskeletal models, and supported by optimisation techniques, is used extensively to estimate muscle and joint reaction forces. However, the solutions of the redundant muscle force sharing problem are sensitive to the detail and modelling assumptions of the models used. This study presents four alternative biomechanical models of the upper limb with different levels of discretisation of muscles by bundles and muscle paths, and their consequences on the estimation of the muscle and joint reaction forces. The muscle force sharing problem is solved for the motions of abduction and anterior flexion, acquired using video imaging, through the minimisation of an objective function describing muscle metabolic energy consumption. While looking for the optimal solution, not only the equations of motion are satisfied but also the stability of the glenohumeral and scapulothoracic joints is preserved. The results show that a lower level of muscle discretisation provides worse estimations regarding the muscle forces. Moreover, the poor discretisation of muscles relevant to the joint in analysis limits the applicability of the biomechanical model. In this study, the biomechanical model of the upper limb describing the infraspinatus by a single bundle could not solve the complete motion of anterior flexion. Despite the small differences in the magnitude of the forces predicted by the biomechanical models with more complex muscular systems, in general, there are no significant variations in the muscular activity of equivalent muscles.     

An EMG-based, muscle driven forward simulation of single support phase of gait

This paper describes the process used to generate lower limb kinematics during single limb stance phase of gait, using musculoskeletal modelling, muscle driven forward simulation and gradient based optimisation techniques (including design of experiment techniques).Initial inputs to the forward simulation process were the normalised quantified muscle activation patterns of 22 muscles, and the initial segmental configuration (both angles and angular velocity) derived from Winter (The biomechanics and motor control of human gait, 1987, University of Waterloo Press, pp. 1-72). Two distinct musculoskeletal models (one including 6 DOF, the other 7 DOF) were defined and a muscle driven forward simulation was implemented.A series of optimisation sequences then were executed to modify the muscle activation patterns and initial segmental configuration, until the system output of the forward simulation approximated the angle data reported by. The accuracy and effectiveness of the analysis sequence proposed and the model response obtained using two distinct musculoskeletal models were verified and analysed with respect to the kinesiology of normal walking.     

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 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.     

Sensitivity of model predictions of muscle function to changes in moment arms and muscle-tendon properties: a Monte-Carlo analysis

Hill-type muscle models are commonly used in musculoskeletal models to estimate muscle forces during human movement. However, the sensitivity of model predictions of muscle function to changes in muscle moment arms and muscle-tendon properties is not well understood. In the present study, a three-dimensional muscle-actuated model of the body was used to evaluate the sensitivity of the function of the major lower limb muscles in accelerating the whole-body center of mass during gait. Monte-Carlo analyses were used to quantify the effects of entire distributions of perturbations in the moment arms and architectural properties of muscles. In most cases, varying the moment arm and architectural properties of a muscle affected the torque generated by that muscle about the joint(s) it spanned as well as the torques generated by adjacent muscles. Muscle function was most sensitive to changes in tendon slack length and least sensitive to changes in muscle moment arm. However, the sensitivity of muscle function to changes in moment arms and architectural properties was highly muscle-specific; muscle function was most sensitive in the cases of gastrocnemius and rectus femoris and insensitive in the cases of hamstrings and the medial sub-region of gluteus maximus. The sensitivity of a muscle's function was influenced by the magnitude of the muscle's force as well as the operating region of the muscle on its force-length curve. These findings have implications for the development of subject-specific models of the human musculoskeletal system.     

Model of loadings acting on the femoral bone during gait

An evaluation of the model of loadings acting on the femoral bone during the whole gait cycle was the main aim of the paper. A computer simulation of the musculoskeletal system based on the gait data collected during gait was used to determine the muscle forces as well as the hip joint reaction. Kinematic parameters as well as the ground reaction force for ninety-nine healthy persons of both sexes (18–36 years old) who had no history of musculoskeletal disease were registered during normal gait with preferred speed and used as inputs for musculoskeletal modelling and numerical simulation with the use of the AnyBody software. Time waveforms of the values of force generated by 21 muscles having attachments on the femoral bone as well as the hip joint reaction force were obtained. Directions of particular forces were presented using a femoral coordinate system. Attachment points for all muscle forces were obtained on the basis of the unscaled standard model with the length of the femur equal to 0.41 m. The presented model of loadings acting on the femoral bone element can be useful for the biomechanical analysis of bone development and remodelling as well as for the optimisation of implant or bone stabilizer design and pre-clinical testing.     

A physiology-based inverse dynamic analysis of human gait using sequential convex programming: a comparative study

This paper presents an enhanced version of the previously proposed physiological inverse approach (PIA) to calculate musculotendon (MT) forces and evaluates the proposed methodology in a comparative study. PIA combines an inverse dynamic analysis with an optimisation approach that imposes muscle physiology and optimises performance over the entire motion. To solve the resulting large-scale, nonlinear optimisation problem, we neglected muscle fibre contraction speed and an approximate quadratic optimisation problem (PIA-QP) was formulated. Conversely, the enhanced version of PIA proposed in this paper takes into account muscle fibre contraction speed. The optimisation problem is solved using a sequential convex programing procedure (PIA-SCP). The comparative study includes PIA-SCP, PIA-QP and two commonly used approaches from the literature: static optimisation (SO) and computed muscle control (CMC). SO and CMC make simplifying assumptions to limit the computational time. Both methods minimise an instantaneous performance criterion. Furthermore, SO does not impose muscle physiology. All methods are applied to a gait cycle of six control subjects. The relative root mean square error averaged over all subjects, ε(RMS), between the joint torques simulated from the optimised activations and the joint torques obtained from the inverse dynamic analysis was about twice as large for SO (ε(RMS) = 86) as compared with CMC (ε(RMS) = 39) and PIA-SCP (ε(RMS) = 50). ε(RMS) was at least twice as large for PIA-QP (ε(RMS) = 197) than for all other methods. As compared with CMC, muscle activation patterns predicted by PIA-SCP better agree with experimental electromyography (EMG). This study shows that imposing muscle physiology as well as globally optimising performance is important to accurately calculate MT forces underlying gait.     

Antagonist muscle co-contraction during a double-leg landing maneuver at two heights

Background: Knee injuries are common during landing activities. Greater landing height increases peak ground reaction forces (GRFs) and loading at the knee joint. As major muscles to stabilize the knee joint, Quadriceps and Hamstring muscles provide internal forces to attenuate the excessive GRF. Despite the number of investigations on the importance of muscle function during landing, the role of landing height on these muscles forces using modeling during landing is not fully investigated. Methods: Participant-specific musculoskeletal models were developed using experimental motion analysis data consisting of anatomic joint motions and GRF from eight male participants performing double-leg drop landing from 30 and 60 cm. Muscle forces were calculated in OpenSim and their differences were analyzed at the instances of high risk during landing i.e. peak GRF for both heights. Results: The maximum knee flexion angle and moments were found significantly higher from a double-leg landing at 60 cm compared to 30 cm. The results showed elevated GRF, and mean muscle forces during landing. At peak GRF, only quadriceps showed significantly greater forces at 60 cm. Hamstring muscle forces did not significantly change at 60 cm compared to 30 cm. Conclusions: Quadriceps and hamstring muscle forces changed at different heights. Since hamstring forces were similar in both landing heights, this could lead to an imbalance between the antagonist muscles, potentially placing the knee at risk of injury if combined with small flexion angles that was not observed at peak GRF in our study. Thus, enhanced neuromuscular training programs strengthening the hamstrings may be required to address this imbalance. These findings may contribute to enhance neuromuscular training programs to prevent knee injuries during landing.     

Antagonistic activity of one-joint muscles in three-dimensions using non-linear optimisation

Non-linear optimisation, such as the type presented by R.D. Crowninshield and R.A. Brand [The prediction of forces in joint structures: Distribution of intersegmental resultants, Exercise Sports Sci. Rev. 9 (1981) 159], has been frequently used to obtain a unique set of muscle forces during human or animal movements. In the past, analytical solutions of this optimisation problem have been presented for single degree-of-freedom models, and planar models with a specific number of muscles and a defined musculoskeletal geometry. Results of these studies have been generalised to three-dimensional problems and for general formulations of the musculoskeletal geometry without corresponding proofs. Here, we extend the general solution of the above non-linear, constrained, planar optimisation problem to three-dimensional systems of arbitrary geometry. We show that there always exists a set of intersegmental moments for which the given static optimisation formulation will predict co-contraction of a pair of antagonistic muscles unless they are exact antagonists. Furthermore, we provide, for a given three-dimensional system consisting of single joint muscles, a method that describes all the possible joint moments that give co-contraction for a given pair of antagonistic muscles.     

Static optimization underestimates antagonist muscle activity at the glenohumeral joint: A musculoskeletal modeling study

Static optimization is commonly employed in musculoskeletal modeling to estimate muscle and joint loading; however, the ability of this approach to predict antagonist muscle activity at the shoulder is poorly understood. Antagonist muscles, which contribute negatively to a net joint moment, are known to be important for maintaining glenohumeral joint stability. This study aimed to compare muscle and joint force predictions from a subject-specific neuromusculoskeletal model of the shoulder driven entirely by measured muscle electromyography (EMG) data with those from a musculoskeletal model employing static optimization. Four healthy adults performed six sub-maximal upper-limb contractions including shoulder abduction, adduction, flexion, extension, internal rotation and external rotation. EMG data were simultaneously measured from 16 shoulder muscles using surface and intramuscular electrodes, and joint motion evaluated using video motion analysis. Muscle and joint forces were calculated using both a calibrated EMG-driven neuromusculoskeletal modeling framework, and musculoskeletal model simulations that employed static optimization. The EMG-driven model predicted antagonistic muscle function for pectoralis major, latissimus dorsi and teres major during abduction and flexion; supraspinatus during adduction; middle deltoid during extension; and subscapularis, pectoralis major and latissimus dorsi during external rotation. In contrast, static optimization neural solutions showed little or no recruitment of these muscles, and preferentially activated agonistic prime movers with large moment arms. As a consequence, glenohumeral joint force calculations varied substantially between models. The findings suggest that static optimization may under-estimate the activity of muscle antagonists, and therefore, their contribution to glenohumeral joint stability.     

Alterations of musculoskeletal models for a more accurate estimation of lower limb joint contact forces during normal gait: A systematic review

Musculoskeletal modelling is a methodology used to investigate joint contact forces during a movement. High accuracy in the estimation of the hip or knee joint contact forces can be obtained with subject-specific models. However, construction of subject-specific models remains time consuming and expensive. The purpose of this systematic review of the literature was to identify what alterations can be made on generic (i.e. literature-based, without any subject-specific measurement other than body size and weight) musculoskeletal models to obtain a better estimation of the joint contact forces. The impact of these alterations on the accuracy of the estimated joint contact forces were appraised.The systematic search yielded to 141 articles and 24 papers were included in the review. Different strategies of alterations were found: skeletal and joint model (e.g. number of degrees of freedom, knee alignment), muscle model (e.g. Hill-type muscle parameters, level of muscular redundancy), and optimisation problem (e.g. objective function, design variables, constraints). All these alterations had an impact on joint contact force accuracy, so demonstrating the potential for improving the model predictions without necessarily involving costly and time consuming medical images. However, due to discrepancies in the reported evidence about this impact and despite a high quality of the reviewed studies, it was not possible to highlight any trend defining which alteration had the largest impact.     

Muscle and external load contribution to knee joint contact loads during normal gait

Large knee adduction moments during gait have been implicated as a mechanical factor related to the progression and severity of tibiofemoral osteoarthritis and it has been proposed that these moments increase the load on the medial compartment of the knee joint. However, this mechanism cannot be validated without taking into account the internal forces and moments generated by the muscles and ligaments, which cannot be easily measured. Previous musculoskeletal models suggest that the medial compartment of the tibiofemoral joint bears the majority of the tibiofemoral load, with the lateral compartment unloaded at times during stance. Yet these models did not utilise explicitly measured muscle activation patterns and measurements from an instrumented prosthesis which do not portray lateral compartment unloading. This paper utilised an EMG-driven model to estimate muscle forces and knee joint contact forces during healthy gait. Results indicate that while the medial compartment does bear the majority of the load during stance, muscles provide sufficient stability to counter the tendency of the external adduction moment to unload the lateral compartment. This stability was predominantly provided by the quadriceps, hamstrings, and gastrocnemii muscles, although the contribution from the tensor fascia latae was also significant. Lateral compartment unloading was not predicted by the EMG-driven model, suggesting that muscle activity patterns provide useful input to estimate muscle and joint contact forces.     

Knee muscle forces during walking and running in patellofemoral pain patients and pain-free controls

One proposed mechanism of patellofemoral pain, increased stress in the joint, is dependent on forces generated by the quadriceps muscles. Describing causal relationships between muscle forces, tissue stresses, and pain is difficult due to the inability to directly measure these variables in vivo. The purpose of this study was to estimate quadriceps forces during walking and running in a group of male and female patients with patellofemoral pain (n=27, 16 female; 11 male) and compare these to pain-free controls (n=16, 8 female; 8 male). Subjects walked and ran at self-selected speeds in a gait laboratory. Lower limb kinematics and electromyography (EMG) data were input to an EMG-driven musculoskeletal model of the knee, which was scaled and calibrated to each individual to estimate forces in 10 muscles surrounding the joint. Compared to controls, the patellofemoral pain group had greater co-contraction of quadriceps and hamstrings (p=0.025) and greater normalized muscle forces during walking, even though the net knee moment was similar between groups. Muscle forces during running were similar between groups, but the net knee extension moment was less in the patellofemoral pain group compared to controls. Females displayed 30–50% greater normalized hamstring and gastrocnemius muscle forces during both walking and running compared to males (p<0.05). These results suggest that some patellofemoral pain patients might experience greater joint contact forces and joint stresses than pain-free subjects. The muscle force data are available as supplementary material.     

Contributions of individual muscles to hip joint contact force in normal walking

The human hip joint withstands high contact forces during daily activity and is therefore susceptible to injury and structural deterioration over time. Knowledge of muscle-force contributions to hip joint loading may assist in the development of strategies to prevent and manage conditions such as osteoarthritis, femoro-acetabular impingement and fracture. The main aim of this study was to determine the contributions of individual muscles to hip contact force in normal walking. Muscle contributions to hip contact force were calculated based on a previously published dynamic optimization solution for normal walking, which provided the time histories of joint motion, ground reaction forces, and muscle forces during the stance and swing phases of gait. The force developed by each muscle plus its contribution to the ground reaction force were used to determine the muscle’s contribution to hip contact force. Muscles were the major contributors to hip contact force, with gravitational and centrifugal forces combined contributing less than 5% of the total contact force. Four muscles that span the hip – gluteus medius, gluteus maximus, iliopsoas, and hamstrings – contributed most significantly to the three components of the hip contact force and hip contact impulse (integral of hip contact force over time). Three muscles that do not span the hip – vasti, soleus, and gastrocnemius – also contributed substantially to hip joint loading. These results provide additional insight into lower-limb muscle function during walking and may also be relevant to studies of cartilage degeneration and bone remodelling at the hip.     

A method of calculating physiologically relevant joint reaction forces during forward dynamic simulations of movement from an existing knee model

In the commonly used SIMM software, which includes a complete musculoskeletal model of the lower limbs, the reaction forces at the knee are computed. These reaction forces represent the bone-on-bone contact forces and the soft tissue forces (e.g. ligaments) other than muscles acting at the joint. In the knee model integrated into this software, a patellotibial joint rather than a patellofemoral joint is defined, and a force acting along the direction of the patellar ligament is not included. Although this knee model results in valid kinematics and muscle moment arms, the reaction forces at the knee calculated do not represent physiologic knee joint reaction forces. Hence our objectives were to develop a method of calculating physiologic knee joint reaction forces using the knee model incorporated into the SIMM software and to demonstrate the differences in the forces returned by SIMM and the physiologic forces in an example. Our method converts the anatomically fictional patellotibial joint into a patellofemoral joint and computes the force in an inextensible patellar ligament. In our example, the rectus femoris was fully excited isometrically, with the knee and hip flexed to 90 degrees . The resulting SIMM tibiofemoral joint reaction force was primarily shear, because the quadriceps force was applied to the tibia via the fictional patellotibial joint. In contrast the physiologic tibiofemoral joint reaction force was primarily compression, because the quadriceps force was applied through the patellar ligament. This result illustrates that the physiologic knee joint reaction forces are profoundly different than the forces returned by SIMM. However physiologic knee joint reaction forces can be computed with postprocessing of SIMM results.