Subject-specific Musculoskeletal Models Research Articles

Prophylactic knee bracing alters lower-limb muscle forces during a double-leg drop landing

Anterior cruciate ligament (ACL) injury can be a painful, debilitating and costly consequence of participating in sporting activities. Prophylactic knee bracing aims to reduce the number and severity of ACL injury, which commonly occurs during landing maneuvers and is more prevalent in female athletes, but a consensus on the effectiveness of prophylactic knee braces has not been established. The lower-limb muscles are believed to play an important role in stabilizing the knee joint. The purpose of this study was to investigate the changes in lower-limb muscle function with prophylactic knee bracing in male and female athletes during landing. Fifteen recreational athletes performed double-leg drop landing tasks from 0.30m and 0.60m with and without a prophylactic knee brace. Motion analysis data were used to create subject-specific musculoskeletal models in OpenSim. Static optimization was performed to calculate the lower-limb muscle forces. A linear mixed model determined that the hamstrings and vasti muscles produced significantly greater flexion and extension torques, respectively, and greater peak muscle forces with bracing. No differences in the timings of peak muscle forces were observed. These findings suggest that prophylactic knee bracing may help to provide stability to the knee joint by increasing the active stiffness of the hamstrings and vasti muscles later in the landing phase rather than by altering the timing of muscle forces. Further studies are necessary to quantify whether prophylactic knee bracing can reduce the load placed on the ACL during intense dynamic movements.

A Subject-Specific EMG-Driven Musculoskeletal Model for Applications in Lower-Limb Rehabilitation Robotics

Robotic devices have great potential in physical therapy owing to their repeatability, reliability and cost economy. However, there are great challenges to realize active control strategy, since the operator’s motion intention is uneasy to be recognized by robotics online. The purpose of this paper is to propose a subject-specific electromyography (EMG)-driven musculoskeletal model to estimate subject’s joint torque in real time, which can be used to detect his/her motion intention by forward dynamics, and then to explore its potential applications in rehabilitation robotics control. The musculoskeletal model uses muscle activation dynamics to extract muscle activation from raw EMG signals, a Hill-type muscle-tendon model to calculate muscle contraction force, and a proposed subject-specific musculoskeletal geometry model to calculate muscular moment arm. The parameters of muscle activation dynamics and muscle-tendon model are identified by off-line optimization methods in order to minimize the differences between the estimated muscular torques and the reference torques. Validation experiments were conducted on six healthy subjects to evaluate the proposed model. Experimental results demonstrated the model’s ability to predict knee joint torque with the coefficient of determination ([Formula: see text] value of [Formula: see text] and the normalized root-mean-square error (RMSE) of [Formula: see text].

Muscle-tendon unit scaling methods of Hill-type musculoskeletal models: An overview.

This article gives an overview of the state of the art in scaling methods of generic Hill-type muscle model parameters in view of different applications and implementation of experimental data. This article establishes the requirements used to alter a generic model toward subject-specific musculoskeletal models. This article aims to improve model transparency by a structured description of scaling methods and the associated limitations in musculoskeletal models and highlight the importance of selecting a scaling method supporting the purpose of the model.

Muscle parameters estimation based on biplanar radiography

The evaluation of muscle and joint forces in vivo is still a challenge. Musculo-Skeletal (musculo-skeletal) models are used to compute forces based on movement analysis. Most of them are built from a scaled-generic model based on cadaver measurements, which provides a low level of personalization, or from Magnetic Resonance Images, which provide a personalized model in lying position. This study proposed an original two steps method to access a subject-specific musculo-skeletal model in 30 min, which is based solely on biplanar X-Rays. First, the subject-specific 3D geometry of bones and skin envelopes were reconstructed from biplanar X-Rays radiography. Then, 2200 corresponding control points were identified between a reference model and the subject-specific X-Rays model. Finally, the shape of 21 lower limb muscles was estimated using a non-linear transformation between the control points in order to fit the muscle shape of the reference model to the X-Rays model. Twelfth musculo-skeletal models were reconstructed and compared to their reference. The muscle volume was not accurately estimated with a standard deviation (SD) ranging from 10 to 68%. However, this method provided an accurate estimation the muscle line of action with a SD of the length difference lower than 2% and a positioning error lower than 20 mm. The moment arm was also well estimated with SD lower than 15% for most muscle, which was significantly better than scaled-generic model for most muscle. This method open the way to a quick modeling method for gait analysis based on biplanar radiography.

Sensitivity of subject-specific models to Hill muscle–tendon model parameters in simulations of gait

Subject-specific musculoskeletal (MS) models of the lower extremity are essential for applications such as predicting the effects of orthopedic surgery. We performed an extensive sensitivity analysis to assess the effects of potential errors in Hill muscle–tendon (MT) model parameters for each of the 56 MT parts contained in a state-of-the-art MS model. We used two metrics, namely a Local Sensitivity Index (LSI) and an Overall Sensitivity Index (OSI), to distinguish the effect of the perturbation on the predicted force produced by the perturbed MT parts and by all the remaining MT parts, respectively, during a simulated gait cycle. Results indicated that sensitivity of the model depended on the specific role of each MT part during gait, and not merely on its size and length. Tendon slack length was the most sensitive parameter, followed by maximal isometric muscle force and optimal muscle fiber length, while nominal pennation angle showed very low sensitivity. The highest sensitivity values were found for the MT parts that act as prime movers of gait (Soleus: average OSI=5.27%, Rectus Femoris: average OSI=4.47%, Gastrocnemius: average OSI=3.77%, Vastus Lateralis: average OSI=1.36%, Biceps Femoris Caput Longum: average OSI=1.06%) and hip stabilizers (Gluteus Medius: average OSI=3.10%, Obturator Internus: average OSI=1.96%, Gluteus Minimus: average OSI=1.40%, Piriformis: average OSI=0.98%), followed by the Peroneal muscles (average OSI=2.20%) and Tibialis Anterior (average OSI=1.78%) some of which were not included in previous sensitivity studies. Finally, the proposed priority list provides quantitative information to indicate which MT parts and which MT parameters should be estimated most accurately to create detailed and reliable subject-specific MS models.

The Role of Arch Compression and Metatarsophalangeal Joint Dynamics in Modulating Plantar Fascia Strain in Running.

Elastic energy returned from passive-elastic structures of the lower limb is fundamental in lowering the mechanical demand on muscles during running. The purpose of this study was to investigate the two length-modulating mechanisms of the plantar fascia, namely medial longitudinal arch compression and metatarsophalangeal joint (MPJ) excursion, and to determine how these mechanisms modulate strain, and thus elastic energy storage/return of the plantar fascia during running. Eighteen runners (9 forefoot and 9 rearfoot strike) performed three treadmill running trials; unrestricted shod, shod with restricted arch compression (via an orthotic-style insert), and barefoot. Three-dimensional motion capture and ground reaction force data were used to calculate lower limb kinematics and kinetics including MPJ angles, moments, powers and work. Estimates of plantar fascia strain due to arch compression and MPJ excursion were derived using a geometric model of the arch and a subject-specific musculoskeletal model of the plantar fascia, respectively. The plantar fascia exhibited a typical elastic stretch-shortening cycle with the majority of strain generated via arch compression. This strategy was similar in fore- and rear-foot strike runners. Restricting arch compression, and hence the elastic-spring function of the arch, was not compensated for by an increase in MPJ-derived strain. In the second half of stance the plantar fascia was found to transfer energy between the MPJ (energy absorption) and the arch (energy production during recoil). This previously unreported energy transfer mechanism reduces the strain required by the plantar fascia in generating useful positive mechanical work at the arch during running.

How sensitive is the deltoid moment arm to humeral offset changes with reverse total shoulder arthroplasty?

Reverse total shoulder arthroplasty commonly treats cuff-deficient or osteoarthritic shoulders not amenable to rotator cuff repair. This study investigates deltoid moment arm sensitivity to variations in the joint center and humeral offset of 3 representative reverse total shoulder arthroplasty subjects. We hypothesized that a superior joint implant placement may exist, indicated by muscle moment arms, compared with the current actual surgical implant configuration. Moment arms for the anterior, lateral, and posterior aspects of the deltoid muscle were determined for 1521 perturbations of the humeral offset location away from the surgical placement in a subject-specific musculoskeletal model with motion defined by subject-specific in vivo abduction kinematics. The humeral offset was varied from its surgical position ±4 mm in the anterior/posterior direction, ±12 mm in the medial/lateral direction, and -10 to 14 mm in the superior/inferior direction. The anterior deltoid moment arm varied in humeral offset and center of rotation up to 20 mm, primarily in the medial/lateral and superior/inferior directions. The lateral deltoid moment arm varied in humeral offset up to 20 mm, primarily in the medial/lateral and anterior/posterior directions. The posterior deltoid moment arm varied up to 15 mm, primarily in early abduction, and was most sensitive to humeral offset changes in the superior/inferior direction. High variations in muscle moment arms were found for all 3 deltoid components, presenting an opportunity to dramatically change the deltoid moment arms through surgical placement of the reverse shoulder components and by varying the overall offset of the humerus. Basic Science Study; Computer Modeling.

How complex should lower-limb joint models be for subject-specific musculoskeletal modeling applications?

How complex should lower-limb joint models be for subject-specific musculoskeletal modeling applications?

Estimation of musculotendon parameters for scaled and subject specific musculoskeletal models using an optimization technique

A challenging aspect of subject specific musculoskeletal modeling is the estimation of muscle parameters, especially optimal fiber length and tendon slack length. In this study, the method for scaling musculotendon parameters published by Winby et al. (2008), J. Biomech. 41, 1682–1688, has been reformulated, generalized and applied to two cases of practical interest: 1) the adjustment of muscle parameters in the entire lower limb following linear scaling of a generic model and 2) their estimation “from scratch” in a subject specific model of the hip joint created from medical images. In the first case, the procedure maintained the muscles׳ operating range between models with mean errors below 2.3% of the reference model normalized fiber length value. In the second case, a subject specific model of the hip joint was created using segmented bone geometries and muscle volumes publicly available for a cadaveric specimen from the Living Human Digital Library (LHDL). Estimated optimal fiber lengths were found to be consistent with those of a previously published dataset for all 27 considered muscle bundles except gracilis. However, computed tendon slack lengths differed from tendon lengths measured in the LHDL cadaver, suggesting that tendon slack length should be determined via optimization in subject-specific applications. Overall, the presented methodology could adjust the parameters of a scaled model and enabled the estimation of muscle parameters in newly created subject specific models. All data used in the analyses are of public domain and a tool implementing the algorithm is available at https://simtk.org/home/opt_muscle_par.

Effect of lower-limb joint models on subject-specific musculoskeletal models and simulations of daily motor activities

Understanding the validity of using musculoskeletal models is critical, making important to assess how model parameters affect predictions. In particular, assumptions on joint models can affect predictions from simulations of movement, and the identification of image-based joints is unavoidably affected by uncertainty that can decrease the benefits of increasing model complexity. We evaluated the effect of different lower-limb joint models on muscle and joint contact forces during four motor tasks, and assessed the sensitivity to the uncertainties in the identification of anatomical four-bar-linkage joints. Three MRI-based musculoskeletal models having different knee and ankle joint models were created and used for the purpose. Model predictions were compared against a baseline model including simpler and widely-adopted joints. In addition, a probabilistic analysis was performed by perturbing four-bar-linkage joint parameters according to their uncertainty. The differences between models depended on the motor task analyzed, and there could be marked differences at peak loading (up to 2.40 BW at the knee and 1.54 BW at the ankle), although they were rather small over the motor task cycles (up to 0.59 BW at the knee and 0.31 BW at the ankle). The model including more degrees of freedom showed more discrepancies in predicted muscle activations compared to measured muscle activity. Further, including image-based four-bar-linkages was robust to simulate walking, chair rise and stair ascent, but not stair descent (peak standard deviation of 2.66 BW), suggesting that joint model complexity should be set according to the imaging dataset available and the intended application, performing sensitivity analyses.

Pediatric obesity and walking duration increase medial tibiofemoral compartment contact forces.

With the high prevalence of pediatric obesity there is a need for structured physical activity during childhood. However, altered tibiofemoral loading during physical activity in obese children likely contribute to their increased risk of orthopedic disorders of the knee. The goal of this study was to determine the effects of pediatric obesity and walking duration on medial and lateral tibiofemoral contact forces. We collected experimental biomechanics data during treadmill walking at 1 m•s(-1) for 20 min in 10 obese and 10 healthy-weight 8-12 year-olds. We created subject-specific musculoskeletal models using radiographic measures of tibiofemoral alignment and centers-of-pressure, and predicted medial and lateral tibiofemoral contact forces at the beginning and end of each trial. Obesity and walking duration affected tibiofemoral loading. At the beginning of the trail, the average percent of the total load passing through the medial compartment during stance was 85% in the obese children and 63% in the healthy-weight children; at the end of the trial, the medial distribution was 90% in the obese children and 72% in the healthy-weight children. Medial compartment loading rates were 1.78 times greater in the obese participants. The medial compartment loading rate increased 17% in both groups at the end compared to the beginning of the trial (p = 0.001). We found a strong linear relationship between body-fat percentage and the medial-lateral load distribution (r(2) = 0.79). Altered tibiofemoral loading during walking in obese children may contribute to their increased risk of knee pain and pathology.

Sensitivity of predicted muscle forces during gait to anatomical variability in musculotendon geometry

Scaled generic musculoskeletal models are commonly used to drive dynamic simulations of motions. It is however, acknowledged that not accounting for variability in musculoskeletal geometry and musculotendon parameters may confound the simulation results, even when analysing control subjects. This study documents the three-dimensional anatomical variability of musculotendon origins and insertions of 33 lower limb muscles determined based on magnetic resonance imaging in six subjects. This anatomical variability was compared to the musculotendon point location in a generic musculoskeletal model. Furthermore, the sensitivity of muscle forces during gait, calculated using static optimization, to perturbations of the musculotendon point location was analyzed with a generic model. More specific, a probabilistic approach was used: for each analyzed musculotendon point, the three-dimensional location was re-sampled with a uniform Latin hypercube method within the anatomical variability and the static optimization problem was then re-solved for all perturbations. We found that musculotendon point locations in the generic model showed only variable correspondences with the anatomical variability. The anatomical variability of musculotendon point location did affect the calculated muscle forces: muscles most sensitive to perturbations within the anatomical variability are iliacus and psoas. Perturbation of the gluteus medius anterior, iliacus and psoas induces the largest concomitant changes in muscle forces of the unperturbed muscles. Therefore, when creating subject-specific musculoskeletal models, these attachment points should be defined accurately. In addition, the size of the anatomical variability of the musculotendon point location was not related to the sensitivity of the calculated muscle forces.

TLEM 2.0 – A comprehensive musculoskeletal geometry dataset for subject-specific modeling of lower extremity

When analyzing complex biomechanical problems such as predicting the effects of orthopedic surgery, subject-specific musculoskeletal models are essential to achieve reliable predictions. The aim of this paper is to present the Twente Lower Extremity Model 2.0, a new comprehensive dataset of the musculoskeletal geometry of the lower extremity, which is based on medical imaging data and dissection performed on the right lower extremity of a fresh male cadaver. Bone, muscle and subcutaneous fat (including skin) volumes were segmented from computed tomography and magnetic resonance images scans. Inertial parameters were estimated from the image-based segmented volumes. A complete cadaver dissection was performed, in which bony landmarks, attachments sites and lines-of-action of 55 muscle actuators and 12 ligaments, bony wrapping surfaces, and joint geometry were measured. The obtained musculoskeletal geometry dataset was finally implemented in the AnyBody Modeling System™ (AnyBody Technology A/S, Aalborg, Denmark), resulting in a model consisting of 12 segments, 11 joints and 21 degrees of freedom, and including 166 muscle–tendon elements for each leg. The new TLEM 2.0 dataset was purposely built to be easily combined with novel image-based scaling techniques, such as bone surface morphing, muscle volume registration and muscle–tendon path identification, in order to obtain subject-specific musculoskeletal models in a quick and accurate way. The complete dataset, including CT and MRI scans and segmented volume and surfaces, is made available at http://www.utwente.nl/ctw/bw/research/projects/TLEMsafe for the biomechanical community, in order to accelerate the development and adoption of subject-specific models on large scale. TLEM 2.0 is freely shared for non-commercial use only, under acceptance of the TLEMsafe Research License Agreement.

1P1-X07 個人差を考慮した手の筋骨格モデル

In this paper, we developed a subject-specific musculoskeletal hand model considering the differences in the inertia properties and muscle force parameters. We measured motion and force data from one subject; the reference hand posture, three postures while the subject was pinching objects with 28, 42 and 56 mm width, and the maximum pinching force for each posture. By using a digital hand model made from motion data and force data for 42 and 56 mm objects, we assigned several properties and parameters in order to make this model subject-specific. We estimated the maximum force while the subject was pinching objects with 28 mm. As a result of the simulation, the maximum force was estimated to be 29.5 N that was almost agreed with the measured maximum force (26.5 N). These results suggest that it is possible to perform musculoskeletal simulation considering individual differences.

Are subject-specific musculoskeletal models robust to the uncertainties in parameter identification?

Subject-specific musculoskeletal modeling can be applied to study musculoskeletal disorders, allowing inclusion of personalized anatomy and properties. Independent of the tools used for model creation, there are unavoidable uncertainties associated with parameter identification, whose effect on model predictions is still not fully understood. The aim of the present study was to analyze the sensitivity of subject-specific model predictions (i.e., joint angles, joint moments, muscle and joint contact forces) during walking to the uncertainties in the identification of body landmark positions, maximum muscle tension and musculotendon geometry. To this aim, we created an MRI-based musculoskeletal model of the lower limbs, defined as a 7-segment, 10-degree-of-freedom articulated linkage, actuated by 84 musculotendon units. We then performed a Monte-Carlo probabilistic analysis perturbing model parameters according to their uncertainty, and solving a typical inverse dynamics and static optimization problem using 500 models that included the different sets of perturbed variable values. Model creation and gait simulations were performed by using freely available software that we developed to standardize the process of model creation, integrate with OpenSim and create probabilistic simulations of movement. The uncertainties in input variables had a moderate effect on model predictions, as muscle and joint contact forces showed maximum standard deviation of 0.3 times body-weight and maximum range of 2.1 times body-weight. In addition, the output variables significantly correlated with few input variables (up to 7 out of 312) across the gait cycle, including the geometry definition of larger muscles and the maximum muscle tension in limited gait portions. Although we found subject-specific models not markedly sensitive to parameter identification, researchers should be aware of the model precision in relation to the intended application. In fact, force predictions could be affected by an uncertainty in the same order of magnitude of its value, although this condition has low probability to occur.

Integrating dynamic stereo-radiography and surface-based motion data for subject-specific musculoskeletal dynamic modeling

This manuscript presents a new subject-specific musculoskeletal dynamic modeling approach that integrates high-accuracy dynamic stereo-radiography (DSX) joint kinematics and surface-based full-body motion data. We illustrate this approach by building a model in OpenSim for a patient who participated in a meniscus transplantation efficacy study, incorporating DSX data of the tibiofemoral joint kinematics. We compared this DSX-incorporated (DSXI) model to a default OpenSim model built using surface-measured data alone. The architectures and parameters of the two models were identical, while the differences in (time-averaged) tibiofemoral kinematics were of the order of magnitude of 10° in rotation and 10mm in translation. Model-predicted tibiofemoral compressive forces and knee muscle activations were compared against literature data acquired from instrumented total knee replacement components (Fregly et al., 2012) and the patient׳s EMG recording. The comparison demonstrated that the incorporation of DSX data improves the veracity of musculoskeletal dynamic modeling.

A comparison of optimisation methods and knee joint degrees of freedom on muscle force predictions during single-leg hop landings

The aim of this paper was to compare the effect of different optimisation methods and different knee joint degrees of freedom (DOF) on muscle force predictions during a single legged hop. Nineteen subjects performed single-legged hopping manoeuvres and subject-specific musculoskeletal models were developed to predict muscle forces during the movement. Muscle forces were predicted using static optimisation (SO) and computed muscle control (CMC) methods using either 1 or 3 DOF knee joint models. All sagittal and transverse plane joint angles calculated using inverse kinematics or CMC in a 1 DOF or 3 DOF knee were well-matched (RMS error<3°). Biarticular muscles (hamstrings, rectus femoris and gastrocnemius) showed more differences in muscle force profiles when comparing between the different muscle prediction approaches where these muscles showed larger time delays for many of the comparisons. The muscle force magnitudes of vasti, gluteus maximus and gluteus medius were not greatly influenced by the choice of muscle force prediction method with low normalised root mean squared errors (<48%) observed in most comparisons. We conclude that SO and CMC can be used to predict lower-limb muscle co-contraction during hopping movements. However, care must be taken in interpreting the magnitude of force predicted in the biarticular muscles and the soleus, especially when using a 1 DOF knee. Despite this limitation, given that SO is a more robust and computationally efficient method for predicting muscle forces than CMC, we suggest that SO can be used in conjunction with musculoskeletal models that have a 1 or 3 DOF knee joint to study the relative differences and the role of muscles during hopping activities in future studies.

Sensitivity of a subject-specific musculoskeletal model to the uncertainties on the joint axes location

Subject-specific musculoskeletal models have become key tools in the clinical decision-making process. However, the sensitivity of the calculated solution to the unavoidable errors committed while deriving the model parameters from the available information is not fully understood. The aim of this study was to calculate the sensitivity of all the kinematics and kinetics variables to the inter-examiner uncertainty in the identification of the lower limb joint models. The study was based on the computer tomography of the entire lower-limb from a single donor and the motion capture from a body-matched volunteer. The hip, the knee and the ankle joint models were defined following the International Society of Biomechanics recommendations. Using a software interface, five expert anatomists identified on the donor's images the necessary bony locations five times with a three-day time interval. A detailed subject-specific musculoskeletal model was taken from an earlier study, and re-formulated to define the joint axes by inputting the necessary bony locations. Gait simulations were run using OpenSim within a Monte Carlo stochastic scheme, where the locations of the bony landmarks were varied randomly according to the estimated distributions. Trends for the joint angles, moments, and the muscle and joint forces did not substantially change after parameter perturbations. The highest variations were as follows: (a) 11° calculated for the hip rotation angle, (b) 1% BW × H calculated for the knee moment and (c) 0.33 BW calculated for the ankle plantarflexor muscles and the ankle joint forces. In conclusion, the identification of the joint axes from clinical images is a robust procedure for human movement modelling and simulation.

Simultaneous global inverse kinematics and geometric parameter identification of human skeletal model from motion capture data

This paper presents a method to identify the geometric parameters of a human skeletal model from motion capture data. For the identification, the joint trajectories have to be computed from the data at the same time. The method solves both the inverse kinematics and the geometric parameter identification simultaneously. In the method, the parameters are modeled as the generalized coordinates of virtual mechanical joints. It solves the large-scale inverse kinematics to compute the generalized coordinates at all given time-series instances and the time-invariant virtual coordinates. Though the method is based on the nonlinear programing, the computation is accelerated by decomposed gradient computation based on recursive Newton–Euler formulation. The method was tested on a 34-DOF human skeletal model using a motion capture system, and the results of the geometric parameters and the time-series coordinates are to be shown. The method was also applied to obtain a subject-specific musculoskeletal model.

Evaluation of a morphing based method to estimate muscle attachment sites of the lower extremity

To generate subject-specific musculoskeletal models for clinical use, the location of muscle attachment sites needs to be estimated with accurate, fast and preferably automated tools. For this purpose, an automatic method was used to estimate the muscle attachment sites of the lower extremity, based on the assumption of a relation between the bone geometry and the location of muscle attachment sites. The aim of this study was to evaluate the accuracy of this morphing based method. Two cadaver dissections were performed to measure the contours of 72 muscle attachment sites on the pelvis, femur, tibia and calcaneus. The geometry of the bones including the muscle attachment sites was morphed from one cadaver to the other and vice versa. For 69% of the muscle attachment sites, the mean distance between the measured and morphed muscle attachment sites was smaller than 15mm. Furthermore, the muscle attachment sites that had relatively large distances had shown low sensitivity to these deviations. Therefore, this morphing based method is a promising tool for estimating subject-specific muscle attachment sites in the lower extremity in a fast and automated manner.