Improving the upper-limb force feasible set evaluation by muscles maximal isometric force identification and cocontraction factors

In order to improve the evaluation of the force feasible set (FFS) of the upper-limb which is of great interest in the biomechanics field, this study proposes two additional techniques. The first one is based on the identification of the maximal isometric force (MIF) of Hill-based muscles models from sEMG and isometric force measurements at the hand. The second one considers muscles cocontraction.The FFS was computed with an upper-limb musculoskeletal model in three different cases. The first one (M1) considered binary muscular activation and a simple MIF scaling method based on the weight and muscle length of the subject. The second one (M2) introduces cocontraction factors determined from sEMG. The third one (M3) considers the cocontraction factors and the MIF identification. Finally, M1, M2 and M3 are compared with end-effector force measurement.M3 outperforms the two other methods on FFS prediction demonstrating the validity and the usefulness of MIF identification and the consideration of the cocontraction factors.     

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.     

Human upper-limb force capacities evaluation with robotic models for ergonomic applications: effect of elbow flexion

The aim of this study was to apply models derived from the robotics field to evaluate the human upper-limb force generation capacity. Four models were compared: the force ellipsoid (FE) and force polytope (FP) based on unit joint torques and the scaled FE (SFE) and scaled FP (SFP) based on maximum isometric joint torques. The four models were assessed from four upper-limb postures with varying elbow flexion (40°, 60°, 80° and 100°) measured by an optoelectronic system and their corresponding isometric joint torques. Ten subjects were recruited. Three specific ellipsoids and polytopes parameters were compared: isotropy, principal force orientation and volume. Isotropy showed that the ellipsoids and polytopes were elongated. The angle between the two ellipsoids main axis and the two polytopes remained low but increased with the elbow flexion. The FE and FP volumes increased and those of SFE and SFP decreased with the elbow flexion. The interest and limits of such models are discussed in the framework of ergonomics and rehabilitation.     

Wheelchair propulsion: Force orientation and amplitude prediction with Recurrent Neural Network

The aim of this study was to use Recurrent Neural Network (RNN) to predict the orientation and amplitude of the applied force during the push phase of manual wheelchair propulsion.Trunk and the right-upper limb kinematics data were assessed with an optoeletronic device (Qualisys) and the force applied on the handrim was recorded with an instrumented wheel (SMARTWheel®). Data acquisitions were performed at 60/80/10/120/140% of the freely chosen frequency at submaximal and maximal conditions. The final database consisted of d = 5708 push phases.The input data were the trunk and right upper-limb kinematics (joint angle, angular velocity and acceleration) and anthropometric data (height, weight, segment length) and the output data were the applied forces orientation and amplitude. A ratio of 70/15/15 was used to train, validate and test the RNN (d_train = 3996, d_validation = 856 and d_test = 856). The angle and amplitude errors between the measured and predicted force was assessed from d_test.Results showed that for most of the push phase (∼70%), the force direction prediction errors were less than 12°. The mean absolute amplitude errors were less than 8 N and the mean absolute amplitude percentage errors were less than 20% for most of the push phase (∼80%).     

A model of open-loop control of equilibrium position and stiffness of the human elbow joint

According to the equilibrium point theory, the control of posture and movement involves the setting of equilibrium joint positions (EP) and the independent modulation of stiffness. One model of EP control, the α-model, posits that stable EPs and stiffness are set open-loop, i.e. without the aid of feedback. The purpose of the present study was to explore for the elbow joint the range over which stable EPs can be set open-loop and to investigate the effect of co-contraction on intrinsic low-frequency elbow joint stiffness (K _ilf). For this purpose, a model of the upper and lower arm was constructed, equipped with Hill-type muscles. At a constant neural input, the isometric force of the contractile element of the muscles depended on both the myofilamentary overlap and the effect of sarcomere length on the sensitivity of myofilaments to [Ca²⁺] (LDCS). The musculoskeletal model, for which the parameters were chosen carefully on the basis of physiological literature, captured the salient isometric properties of the muscles spanning the elbow joint. It was found that stable open-loop EPs could be achieved over the whole range of motion of the elbow joint and that K _ilf, which ranged from 18 to 42 N m·rad⁻¹, could be independently controlled. In the model, LDCS contributed substantially to K _ilf (up to 25 N m·rad⁻¹) and caused K _ilf to peak at a sub-maximal level of co-contraction.     

A musculoskeletal model of the hand and wrist: model definition and evaluation

Abstract

To improve our understanding on the neuromechanics of finger movements, a comprehensive musculoskeletal model is needed. The aim of this study was to build a musculoskeletal model of the hand and wrist, based on one consistent data set of the relevant anatomical parameters. We built and tested a model including the hand and wrist segments, as well as the muscles of the forearm and hand in OpenSim. In total, the model comprises 19 segments (with the carpal bones modeled as one segment) with 23 degrees of freedom and 43 muscles. All required anatomical input data, including bone masses and inertias, joint axis positions and orientations as well as muscle morphological parameters (i.e. PCSA, mass, optimal fiber length and tendon length) were obtained from one cadaver of which the data set was recently published. Model validity was investigated by first comparing computed muscle moment arms at the index finger metacarpophalangeal (MCP) joint and wrist joint to published reference values. Secondly, the muscle forces during pinching were computed using static optimization and compared to previously measured intraoperative reference values. Computed and measured moment arms of muscles at both index MCP and wrist showed high correlation coefficients (r?=?0.88 averaged across all muscles) and modest root mean square deviation (RMSD?=?23% averaged across all muscles). Computed extrinsic flexor forces of the index finger during index pinch task were within one standard deviation of previously measured in-vivo tendon forces. These results provide an indication of model validity for use in estimating muscle forces during static tasks.     

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.     

Development of an anatomically based whole‐body musculoskeletal model of the Japanese macaque (Macaca fuscata)

We constructed a three‐dimensional whole‐body musculoskeletal model of the Japanese macaque (Macaca fuscata) based on computed tomography and dissection of a cadaver. The skeleton was modeled as a chain of 20 bone segments connected by joints. Joint centers and rotational axes were estimated by joint morphology based on joint surface approximation using a quadric function. The path of each muscle was defined by a line segment connecting origin to insertion through an intermediary point if necessary. Mass and fascicle length of each were systematically recorded to calculate physiological cross‐sectional area to estimate the capacity of each muscle to generate force. Using this anatomically accurate model, muscle moment arms and force vectors generated by individual limb muscles at the foot and hand were calculated to computationally predict muscle functions. Furthermore, three‐dimensional whole‐body musculoskeletal kinematics of the Japanese macaque was reconstructed from ordinary video sequences based on this model and a model‐based matching technique. The results showed that the proposed model can successfully reconstruct and visualize anatomically reasonable, natural musculoskeletal motion of the Japanese macaque during quadrupedal/bipedal locomotion, demonstrating the validity and efficacy of the constructed musculoskeletal model. The present biologically relevant model may serve as a useful tool for comprehensive understanding of the design principles of the musculoskeletal system and the control mechanisms for locomotion in the Japanese macaque and other primates. Am J Phys Anthropol, 2009. © 2008 Wiley‐Liss, Inc.     

Alterations in peripheral joint muscle force control in adults with musculoskeletal disease,injury, surgery,or arthroplasty: A systematic review and meta-analysis

PurposeTo systematically review and analyse whether musculoskeletal conditions affect peripheral joint muscle force control (i.e. magnitude and/or complexity of force fluctuations).MethodsA literature search was conducted using MEDLINE, CINAHL and SPORTDiscus databases (from inception-8th April 2021) for studies involving: 1) participants with musculoskeletal disease, injury, surgery, or arthroplasty in the peripheral joints of the upper/lower limb; 2) comparison with an unaffected control group or unaffected contralateral limb; and 3) measures of the magnitude and/or complexity of force fluctuations during targeted isometric contractions. The methodological quality of studies was evaluated using a modified Downs and Black Quality Index. Studies were combined using the standardized mean difference (SMD) in a random-effects model.Results14 studies (investigating 694 participants) were included in the meta-analysis. There was a significant effect of musculoskeletal conditions on peripheral joint muscle force coefficient of variation (CV; SMD = 0.19 [95 % CI 0.06, 0.32]), whereby individuals with musculoskeletal conditions exhibited greater CV than controls. Subgroup analyses revealed that CV was only greater: 1) when comparison was made between symptomatic and asymptomatic individuals (rather than between affected and contralateral limbs; SMD = 0.22 [95 % CI 0.07, 0.38]); 2) for conditions of the knee (SMD = 0.29 [95 % CI 0.14, 0.44]); and 3) for ACL injury post-surgery (SMD = 0.56 [95 % CI 0.36, 0.75]).ConclusionMusculoskeletal conditions result in an increase in peripheral joint muscle force CV, with this effect dependent on study design, peripheral joint, and surgical status. The greater force CV is indicative of decreased force steadiness and could have implications for long-term tissue health/day-to-day function.     

Musculoskeletal modeling of user groups for virtual product and process development

Abstract

Although biomechanical digital human models find their way into virtual engineering processes, biomechanical considerations are currently still unrecognized to a large extent. One major obstacle lies in the fact that even though subject-specific modeling procedures are developed, virtual user groups or populations are still missing. The objective of this contribution is to create such groups of musculoskeletal models. Therefore, a modeling procedure based upon population data is described. First of all, two generic three-dimensional models, one with female and one with male average anthropometric dimensions, were obtained. These models constitute the starting point for the following model adjustment phases. Evenly distributed dimensionless values for gender, age, height, mass, range of motion and strength are sampled and translated into more expressive parameters for the mentioned modeling domains serving as input data for the creation of each individual model of the desired population or user group. The most sophisticated step of the adaption is the strength mapping aiming to create models matching arbitrary target joint torques. Finally, the models’ maximal strength is assessed in a manual material handling task and compared to empirical strength data. The approach is shown using the example of the German population.     

Joint contact loading in forefoot and rearfoot strike patterns during running

Research concerning forefoot strike pattern (FFS) versus rearfoot strike pattern (RFS) running has focused on the ground reaction force even though internal joint contact forces are a more direct measure of the loads responsible for injury. The main purpose of this study was to determine the internal loading of the joints for each strike pattern. A secondary purpose was to determine if converted FFS and RFS runners can adequately represent habitual runners with regards to the internal joint loading. Using inverse dynamics to calculate the net joint moments and reaction forces and optimization techniques to estimate muscle forces, we determined the axial compressive loading at the ankle, knee, and hip. Subjects consisted of 15 habitual FFS and 15 habitual RFS competitive runners. Each subject ran at a preferred running velocity with their habitual strike pattern and then converted to the opposite strike pattern. Plantar flexor muscle forces and net ankle joint moments were greater in the FFS running compared to the RFS running during the first half of the stance phase. The average contact forces during this period increased by 41.7% at the ankle and 14.4% at the knee joint during FFS running. Peak ankle joint contact force was 1.5 body weights greater during FFS running (p<0.05). There was no evidence to support a difference between habitual and converted running for joint contact forces. The increased loading at the ankle joint for FFS is an area of concern for individuals considering altering their foot strike pattern.     

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.     

Which data should be tracked in forward-dynamic optimisation to best predict muscle forces in a pathological co-contraction case?

The choice of the cost-function for predicting muscle forces during a movement remains a challenge, especially in patients with neuromuscular disorders. Forward dynamics-based optimisations mainly track joint kinematics or torques, combined with a least-excitation criterion. Tracking marker trajectories and/or electromyography (EMG) has rarely been proposed. Our objective was to determine the best tracking objective-function to accurately predict the upper-limb muscle forces. A musculoskeletal model was created and EMG was simulated to obtain a reference movement – a shoulder abduction. A Gaussian noise (mean = 0; standard deviation = 15%) was added to the simulated EMG. Another noise – corresponding to the actual soft tissue artefacts (STA) of experimental shoulder abduction movements – was added to the trajectories of the markers placed on the model. Muscle forces were estimated from these noisy data, using forward dynamics assisted by six non-linear least-squared objective-functions. These functions involved the tracking of marker trajectories, joint angles or torques, with and without EMG-tracking. All six approaches used the same musculoskeletal model and were solved using a direct multiple shooting algorithm. Finally, the predicted joint angles, muscle forces and activations were compared to the reference values, using root-mean-square errors (RMSe) and biases. The force RMSe of the approach tracking both marker trajectories and EMG (18.45 ± 12.60 N) was almost five times lower than the one of the approach tracking only joint angles (82.37 ± 66.26 N) or torques (85.10 ± 116.40 N). Therefore, using EMG as a complementary tracking-data in forward dynamics seems to be promising for the estimation of muscle forces.     

Effect of body weight support variation on muscle activities during robot assisted gait: a dynamic simulation study

Background and Objectives: While body weight support (BWS) intonation is vital during conventional gait training of neurologically challenged subjects, it is important to evaluate its effect during robot assisted gait training. In the present research we have studied the effect of BWS intonation on muscle activities during robotic gait training using dynamic simulations. Methods: Two dimensional (2-D) musculoskeletal model of human gait was developed conjointly with another 2-D model of a robotic orthosis capable of actuating hip, knee and ankle joints simultaneously. The musculoskeletal model consists of eight major muscle groups namely; soleus (SOL), gastrocnemius (GAS), tibialis anterior (TA), hamstrings (HAM), vasti (VAS), gluteus maximus (GLU), uniarticular hip flexors (iliopsoas, IP), and Rectus Femoris (RF). BWS was provided at levels of 0, 20, 40 and 60% during the simulations. In order to obtain a feasible set of muscle activities during subsequent gait cycles, an inverse dynamics algorithm along with a quadratic minimization algorithm was implemented. Results: The dynamic parameters of the robot assisted human gait such as joint angle trajectories, ground contact force (GCF), human limb joint torques and robot induced torques at different levels of BWS were derived. The patterns of muscle activities at variable BWS were derived and analysed. For most part of the gait cycle (GC) the muscle activation patterns are quite similar for all levels of BWS as is apparent from the mean of muscle activities for the complete GC. Conclusions: Effect of BWS variation during robot assisted gait on muscle activities was studied by developing dynamic simulation. It is expected that the proposed dynamic simulation approach will provide important inferences and information about the muscle function variations consequent upon a change in BWS during robot assisted gait. This information shall be quite important while investigating the influence of BWS intonation on neuromuscular parameters of interest during robotic gait training.     

Subject-specific geometry affects acetabular contact pressure during gait more than subject-specific loading patterns

Abstract

Finite element modeling (FEM) can predict hip cartilage contact mechanics. This study investigated how subject-specific boundary conditions and joint geometry affect acetabular cartilage contact mechanics using a multi-scale workflow. For two healthy subjects, musculoskeletal models calculated subject-specific hip kinematics and loading, which were used as boundary conditions for FEM. Cartilage contact mechanics were predicted using either generic or subject-specific FEM and boundary conditions. A subject-specific mesh resulted in a more lateral contact. Effects of subject-specific boundary conditions varied between both subjects. Results highlight the complex interplay between loading and kinematics and their effect on cartilage contact mechanics.     

VAT=TAAT‐SAAT: Innovative anthropometric model to predict visceral adipose tissue without resort to CT‐Scan or DXA

Objective:

To investigate whether a combination of a selected but limited number of anthropometric measurements predicts visceral adipose tissue (VAT) better than other anthropometric measurements, without resort to medical imaging.

Hypothesis:

Abdominal anthropometric measurements are total abdominal adipose tissue indicators and global measures of VAT and SAAT (subcutaneous abdominal adipose tissue). Therefore, subtracting the anthropometric measurement the more correlated possible with SAAT while being the least correlated possible with VAT, from the most correlated abdominal anthropometric measurement with VAT while being highly correlated with TAAT, may better predict VAT.

Design and Methods:

BMI participants' range was from 16.3 to 52.9 kg m^?2. Anthropometric and abdominal adipose tissues data by computed tomography (CT‐Scan) were available in 253 patients (18‐78 years) (CHU Nord, Marseille) and used to develop the anthropometric VAT prediction models.

Results:

Subtraction of proximal thigh circumference from waist circumference, adjusted to age and/or BMI, predicts better VAT (Women: VAT = 2.15 × Waist C ? 3.63 × Proximal Thigh C + 1.46 × Age + 6.22 × BMI ? 92.713; R² = 0.836. Men: VAT = 6 × Waist C ? 4.41 × proximal thigh C + 1.19 × Age ? 213.65; R² = 0.803) than the best single anthropometric measurement or the association of two anthropometric measurements highly correlated with VAT. Both multivariate models showed no collinearity problem. Selected models demonstrate high sensitivity (97.7% in women, 100% in men). Similar predictive abilities were observed in the validation sample (Women: R² = 76%; Men: R² = 70%). Bland and Altman method showed no systematic estimation error of VAT.

Conclusion:

Validated in a large range of age and BMI, our results suggest the usefulness of the anthropometric selected models to predict VAT in Europides (South of France).

     

Prediction of ground reaction forces during gait based on kinematics and a neural network model

Kinetic information during human gait can be estimated with inverse dynamics, which is based on anthropometric, kinematic, and ground reaction data. While collecting ground reaction data with a force plate is useful, it is costly and requires regulated space. The goal of this study was to propose a new, accurate methodology for predicting ground reaction forces (GRFs) during level walking without the help of a force plate. To predict GRFs without a force plate, the traditional method of Newtonian mechanics was used for the single support phase. In addition, an artificial neural network (ANN) model was applied for the double support phase to solve statically indeterminate structure problems. The input variables of the ANN model, which were selected to have both dependency and independency, were limited to the trajectory, velocity, and acceleration of the whole segment's mass centre to minimise errors. The predicted GRFs were validated with actual GRFs through a ten-fold cross-validation method, and the correlation coefficients (R) for the ground forces were 0.918 in the medial–lateral axis, 0.985 in the anterior–posterior axis, and 0.991 in the vertical axis during gait. The ground moments were 0.987 in the sagittal plane, 0.841 in the frontal plane, and 0.868 in the transverse plane during gait. The high correlation coefficients(R) are due to the improvement of the prediction rate in the double support phase. This study also proved the possibility of calculating joint forces and moments based on the GRFs predicted with the proposed new hybrid method. Data generated with the proposed method may thus be used instead of raw GRF data in gait analysis and in calculating joint dynamic data using inverse dynamics.     

Can the non-pre-whitening model observer,including aspects of the human visual system,predict human observer performance in mammography?

PurposeIn mammography, images are processed prior to display. Current methodologies based on physical image quality measurements are however not designed for the evaluation of processed images. Model observers (MO) might be suitable for this evaluation. The aim of this study was to investigate whether the non-pre-whitening (NPW) MO can be used to predict human observer performance in mammography-like images by including different aspects of the human visual system (HVS).MethodsThe correlation between human and NPW MO performance has been investigated for the detection of disk shaped objects in simulated white noise (WN) and clustered lumpy backgrounds (CLB), representing quantum noise limited and mammography-like images respectively. The images were scored by the MO and five human observers in a 2-alternative forced choice experiment.ResultsFor WN images it was found that the log likelihood ratio (R_LR²), which expresses the goodness of fit, was highest (0.44) for the NPW MO without addition of HVS aspects. For CLB the R_LR² improved from 0.46 to 0.65 with addition of HVS aspects. The correlation was affected by object size and background.ConclusionsThis study shows that by including aspects of the HVS, the performance of the NPW MO can be improved to better predict human observer performance. This demonstrates that the NPW MO has potential for image quality assessment. However, due to the dependencies found in the correlation, the NPW MO can only be used for image quality assessment for a limited range of object sizes and background variability.     

Robustness of kinematic weighting and scaling concepts for musculoskeletal simulation

Musculoskeletal modelling is widely used to estimate internal loading conditions. In order to optimise robustness and reduce errors between the subject-specific reference motion data (RMD) and the musculoskeletal simulation, 90 permutations of kinetic and kinematic data were analysed during split squats. A ranking for the scaling and kinematic weighting concepts based on the RMS errors when including functional centres of rotation (fCoRs), joint angles, and skin markers, revealed that analyses should include fCoR in the scaling and the simulation processes, as well as an automated weighting procedure including all attached skin markers for optimal registration of the musculoskeletal model to the RMD.     

Using two palpable measurements improves the subject-specific femoral modeling

Subject-specific musculoskeletal models are essential to biomedical research and clinical applications, such as customized joint replacement, computer-aided surgical planning, gait analysis and automated segmentation. Generating these models from CT or magnetic resonance imaging (MRI) is time and resource intensive, requiring special skills. Therefore, in many studies individual bone models are approximated by scaling a generic template. Thus, the primary goal of this study was to determine a set of clinically available parameters (palpable measures and demographic data) that could improve the prediction of femoral dimensions, as compared to predicting these variables using uniform scaling based on palpable length. Similar to previous non-homogenous anthropometric scaling methods, the non-homogenous scaling method proposed in this study improved the prediction over uniform scaling of five key femoral measures. Homogenous scaling forces all dimensions of an object to be scaled equally, whereas non-homogenous scaling allows the dimensions to be scaled independently. The largest improvement was in femoral depth, where the coefficient of determination (r²) improved from 0.22 (homogenous) to 0.60 (non-homogeneous). In general, the major advantage of this non-homogenous scaling method is its ability to support the accurate and rapid generation of subject-specific femoral models since all parameters can be collected clinically, without imaging or invasive methods.