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.

[1]  Richard J. Beckman,et al.  A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.

[2]  C. E. Clauser,et al.  Weight, volume, and center of mass of segments of the human body , 1969 .

[3]  Marco Viceconti,et al.  Muscle discretization affects the loading transferred to bones in lower-limb musculoskeletal models , 2012, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[4]  S. Delp,et al.  A modeling framework to estimate patellofemoral joint cartilage stress in vivo. , 2005, Medicine and science in sports and exercise.

[5]  S. Delp,et al.  Three-Dimensional Representation of Complex Muscle Architectures and Geometries , 2005, Annals of Biomedical Engineering.

[6]  Marcus G Pandy,et al.  Accuracy of generic musculoskeletal models in predicting the functional roles of muscles in human gait. , 2011, Journal of biomechanics.

[7]  Marcus G Pandy,et al.  Muscle and joint function in human locomotion. , 2010, Annual review of biomedical engineering.

[8]  David W. Wagner,et al.  Consistency Among Musculoskeletal Models: Caveat Utilitor , 2013, Annals of Biomedical Engineering.

[9]  Ilse Jonkers,et al.  Influence of weak hip abductor muscles on joint contact forces during normal walking: probabilistic modeling analysis. , 2013, Journal of biomechanics.

[10]  Walter Herzog,et al.  Model-based estimation of muscle forces exerted during movements. , 2007, Clinical biomechanics.

[11]  Alberto Leardini,et al.  Femoral loads during gait in a patient with massive skeletal reconstruction. , 2012, Clinical biomechanics.

[12]  Rachid Aissaoui,et al.  Personalized body segment parameters from biplanar low-dose radiography , 2005, IEEE Transactions on Biomedical Engineering.

[13]  Ayman Habib,et al.  OpenSim: Open-Source Software to Create and Analyze Dynamic Simulations of Movement , 2007, IEEE Transactions on Biomedical Engineering.

[14]  O. Svensson,et al.  The axis of rotation of the ankle joint. , 1989, The Journal of bone and joint surgery. British volume.

[15]  Hartmut Witte,et al.  ISB recommendation on definitions of joint coordinate system of various joints for the reporting of human joint motion--part I: ankle, hip, and spine. International Society of Biomechanics. , 2002, Journal of biomechanics.

[16]  H. Koopman,et al.  Sensitivity of subject-specific models to errors in musculo-skeletal geometry. , 2012, Journal of biomechanics.

[17]  Marcus G Pandy,et al.  A mass-length scaling law for modeling muscle strength in the lower limb. , 2011, Journal of biomechanics.

[18]  Marco Viceconti,et al.  Sensitivity of a subject-specific musculoskeletal model to the uncertainties on the joint axes location , 2015, Computer methods in biomechanics and biomedical engineering.

[19]  P. Suetens,et al.  Level of subject-specific detail in musculoskeletal models affects hip moment arm length calculation during gait in pediatric subjects with increased femoral anteversion. , 2011, Journal of biomechanics.

[20]  Bryan Buchholz,et al.  ISB recommendation on definitions of joint coordinate systems of various joints for the reporting of human joint motion--Part II: shoulder, elbow, wrist and hand. , 2005, Journal of biomechanics.

[21]  J. Langenderfer,et al.  Probabilistic Modeling of Knee Muscle Moment Arms: Effects of Methods, Origin–Insertion, and Kinematic Variability , 2007, Annals of Biomedical Engineering.

[22]  Anthony G Schache,et al.  Potential of lower-limb muscles to accelerate the body during cerebral palsy gait. , 2012, Gait & posture.

[23]  M G Pandy,et al.  Static and dynamic optimization solutions for gait are practically equivalent. , 2001, Journal of biomechanics.

[24]  Paul Suetens,et al.  Image Based Musculoskeletal Modeling Allows Personalized Biomechanical Analysis of Gait , 2006, ISBMS.

[25]  Benjamin J Fregly,et al.  Design of Optimal Treatments for Neuromusculoskeletal Disorders using Patient-Specific Multibody Dynamic Models. , 2009, International journal for computational vision and biomechanics.

[26]  M. Pandy,et al.  Sensitivity of model predictions of muscle function to changes in moment arms and muscle-tendon properties: a Monte-Carlo analysis. , 2012, Journal of biomechanics.

[27]  M G Pandy,et al.  Integrating modelling and experiments to assess dynamic musculoskeletal function in humans , 2006, Experimental physiology.

[28]  S. Delp,et al.  How robust is human gait to muscle weakness? , 2011, Gait & posture.

[29]  Massimo Sartori,et al.  Subject-specific knee joint geometry improves predictions of medial tibiofemoral contact forces. , 2013, Journal of biomechanics.

[30]  F.E. Zajac,et al.  An interactive graphics-based model of the lower extremity to study orthopaedic surgical procedures , 1990, IEEE Transactions on Biomedical Engineering.

[31]  A Leardini,et al.  Position and orientation in space of bones during movement: anatomical frame definition and determination. , 1995, Clinical biomechanics.

[32]  G Van der Perre,et al.  Subject-specific hip geometry and hip joint centre location affects calculated contact forces at the hip during gait. , 2009, Journal of biomechanics.

[33]  Paul Suetens,et al.  Calculated moment-arm and muscle-tendon lengths during gait differ substantially using MR based versus rescaled generic lower-limb musculoskeletal models. , 2008, Gait & posture.

[34]  I Jonkers,et al.  Sensitivity of dynamic simulations of gait and dynamometer experiments to hill muscle model parameters of knee flexors and extensors. , 2010, Journal of biomechanics.

[35]  Paul J. Rullkoetter,et al.  An efficient probabilistic methodology for incorporating uncertainty in body segment parameters and anatomical landmarks in joint loadings estimated from inverse dynamics. , 2008, Journal of biomechanical engineering.

[36]  David G Lloyd,et al.  Neuromusculoskeletal modeling: estimation of muscle forces and joint moments and movements from measurements of neural command. , 2004, Journal of applied biomechanics.

[37]  Alberto Leardini,et al.  Soft tissue artifact compensation in knee kinematics by double anatomical landmark calibration: performance of a novel method during selected motor tasks , 2005, IEEE Transactions on Biomedical Engineering.

[38]  I. Jonkers,et al.  Relation between subject-specific hip joint loading, stress distribution in the proximal femur and bone mineral density changes after total hip replacement. , 2008, Journal of biomechanics.

[39]  G Van der Perre,et al.  Aberrant pelvis and hip kinematics impair hip loading before and after total hip replacement. , 2009, Gait & posture.

[40]  Scott L. Delp,et al.  A Model of the Lower Limb for Analysis of Human Movement , 2010, Annals of Biomedical Engineering.

[41]  B. Beynnon,et al.  The Transepicondylar Axis Approximates the Optimal Flexion Axis of the Knee , 1998, Clinical orthopaedics and related research.

[42]  Marco Viceconti,et al.  Virtual palpation of skeletal landmarks with multimodal display interfaces , 2007, Medical informatics and the Internet in medicine.

[43]  Marcus G Pandy,et al.  Grand challenge competition to predict in vivo knee loads , 2012, Journal of orthopaedic research : official publication of the Orthopaedic Research Society.

[44]  A. Leardini,et al.  Data management in gait analysis for clinical applications. , 1998, Clinical biomechanics.

[45]  M G Pandy,et al.  Altered hip muscle forces during gait in people with patellofemoral osteoarthritis. , 2012, Osteoarthritis and cartilage.

[46]  J. Higginson,et al.  Sensitivity of estimated muscle force in forward simulation of normal walking. , 2010, Journal of applied biomechanics.

[47]  M. D. McKay,et al.  A comparison of three methods for selecting values of input variables in the analysis of output from a computer code , 2000 .