Research Papers

Neuromusculoskeletal Model Calibration Significantly Affects Predicted Knee Contact Forces for Walking

[+] Author and Article Information
Gil Serrancolí

Department of Mechanical Engineering and
Biomedical Engineering Research Centre,
Universitat Politècnica de Catalunya,
Barcelona, Catalunya 08028, Spain
e-mail: gilserrancoli@hotmail.com

Allison L. Kinney

Department of Mechanical and
Aerospace Engineering,
University of Dayton,
Dayton, OH 45469
e-mail: akinney2@udayton.edu

Benjamin J. Fregly

Department of Mechanical and
Aerospace Engineering,
University of Florida,
Gainesville, FL 32611
e-mail: fregly@ufl.edu

Josep M. Font-Llagunes

Department of Mechanical Engineering and
Biomedical Engineering Research Centre,
Universitat Politècnica de Catalunya,
Av. Diagonal 647,
Barcelona, Catalunya 08028, Spain
e-mail: josep.m.font@upc.edu

1Corresponding author.

Manuscript received May 24, 2015; final manuscript received May 10, 2016; published online June 13, 2016. Assoc. Editor: Silvia Blemker.

J Biomech Eng 138(8), 081001 (Jun 13, 2016) (11 pages) Paper No: BIO-15-1258; doi: 10.1115/1.4033673 History: Received May 24, 2015; Revised May 10, 2016

Though walking impairments are prevalent in society, clinical treatments are often ineffective at restoring lost function. For this reason, researchers have begun to explore the use of patient-specific computational walking models to develop more effective treatments. However, the accuracy with which models can predict internal body forces in muscles and across joints depends on how well relevant model parameter values can be calibrated for the patient. This study investigated how knowledge of internal knee contact forces affects calibration of neuromusculoskeletal model parameter values and subsequent prediction of internal knee contact and leg muscle forces during walking. Model calibration was performed using a novel two-level optimization procedure applied to six normal walking trials from the Fourth Grand Challenge Competition to Predict In Vivo Knee Loads. The outer-level optimization adjusted time-invariant model parameter values to minimize passive muscle forces, reserve actuator moments, and model parameter value changes with (Approach A) and without (Approach B) tracking of experimental knee contact forces. Using the current guess for model parameter values but no knee contact force information, the inner-level optimization predicted time-varying muscle activations that were close to experimental muscle synergy patterns and consistent with the experimental inverse dynamic loads (both approaches). For all the six gait trials, Approach A predicted knee contact forces with high accuracy for both compartments (average correlation coefficient r = 0.99 and root mean square error (RMSE) = 52.6 N medial; average r = 0.95 and RMSE = 56.6 N lateral). In contrast, Approach B overpredicted contact force magnitude for both compartments (average RMSE = 323 N medial and 348 N lateral) and poorly matched contact force shape for the lateral compartment (average r = 0.90 medial and −0.10 lateral). Approach B had statistically higher lateral muscle forces and lateral optimal muscle fiber lengths but lower medial, central, and lateral normalized muscle fiber lengths compared to Approach A. These findings suggest that poorly calibrated model parameter values may be a major factor limiting the ability of neuromusculoskeletal models to predict knee contact and leg muscle forces accurately for walking.

Copyright © 2016 by ASME
Your Session has timed out. Please sign back in to continue.


Vos, T. , Flaxman, A. D. , Naghavi, M. , Lozano, R. , Michaud, C. , Ezzati, M. , Shibuya, K. , et al. ., 2012, “ Years Lived With Disability (YLDs) for 1160 Sequelae of 289 Diseases and Injuries 1990–2010: A Systematic Analysis for the Global Burden of Disease Study 2010,” Lancet, 380(9859), pp. 2163–2196. [CrossRef] [PubMed]
“World Health Organization” [Online]. Available: http://www.who.int/en/
Verghese, J. , LeValley, A. , Hall, C. B. , Katz, M. J. , Ambrose, A. F. , and Lipton, R. B. , 2006, “ Epidemiology of Gait Disorders in Community Residing Older Adults,” J. Am. Geriatr. Soc., 54(2), pp. 255–261. [CrossRef] [PubMed]
States, R. A. , Pappas, E. , and Salem, Y. , 2009, “ Overground Physical Therapy Gait Training for Chronic Stroke Patients With Mobility Deficits,” Stroke, 40(11), pp. e627–e628. [CrossRef]
Buckwalter, J. A. , Stanish, W. D. , Rosier, R. N. , Schenk, R. C. , Dennis, D. A. , and Coutts, R. D. , 2001, “ The Increasing Need for Nonoperative Treatment of Patients With Osteoarthritis,” Clin. Orthop. Relat. R., 385, pp. 36–45. [CrossRef]
Mizner, R. L. , and Snyder-Mackler, L. , 2005, “ Altered Loading During Walking and Sit-to-Stand is Affected by Quadriceps Weakness After Total Knee Arthroplasty,” J. Orthop. Res., 23(5), pp. 1083–1090. [CrossRef] [PubMed]
Fregly, B. J. , Reinbolt, J. A. , Rooney, K. L. , Mitchell, K. H. , and Chmielewski, T. L. , 2007, “ Design of Patient-Specific Gait Modifications for Knee Osteoarthritis Rehabilitation,” IEEE Trans. Biomed. Eng., 54(9), pp. 1687–1695. [CrossRef] [PubMed]
Ackermann, M. , 2008, “ Dynamics and Energetics of Walking With Prostheses,” Ph.D. dissertation, University of Stuttgart, Stuttgart, Germany.
Erdemir, A. , McLean, S. , Herzog, W. , and Van den Bogert, A. J. , 2007, “ Model-Based Estimation of Muscle Forces Exerted During Movements,” Clin. Biomech., 22(2), pp. 131–154. [CrossRef]
Gerus, P. , Sartori, M. , Besier, T. F. , Fregly, B. J. , Delp, S. L. , Banks, S. A. , Pandy, M. G. , D'Lima, D. D. , and Lloyd, D. G. , 2013, “ Subject-Specific Knee Joint Geometry Improves Predictions of Medial Tibiofemoral Contact Forces,” J. Biomech., 46(16), pp. 2778–2786. [CrossRef] [PubMed]
Wesseling, M. , Derikx, L. C. , De Groote, F. , Bartels, W. , Meyer, C. , Verdonschot, N. , and Jonkers, I. , 2015, “ Muscle Optimization Techniques Impact the Magnitude of Calculated Hip Joint Contact Forces,” J. Orthop. Res., 33(3), pp. 430–438. [CrossRef] [PubMed]
Arnold, E. M. , Ward, S. R. , Lieber, R. L. , and Delp, S. L. , 2010, “ A Model of the Lower Limb for Analysis of Human Movement,” Ann. Biomed. Eng., 38(2), pp. 269–279. [CrossRef] [PubMed]
Klein Horsman, M. D. , Koopman, H. F. J. M. , Van der Helm, F. C. T. , Poliacu Prosé, L. , and Veeger, H. E. J. , 2007, “ Morphological Muscle and Joint Parameters for Musculoskeletal Modelling of the Lower Extremity,” Clin. Biomech., 22(2), pp. 239–247. [CrossRef]
Lloyd, D. G. , and Besier, T. F. , 2003, “ An EMG-Driven Musculoskeletal Model to Estimate Muscle Forces and Knee Joint Moments In Vivo,” J. Biomech., 36(6), pp. 765–776. [CrossRef] [PubMed]
Shao, Q. , Bassett, D. N. , Manal, K. , and Buchanan, T. S. , 2009, “ An EMG-Driven Model to Estimate Muscle Forces and Joint Moments in Stroke Patients,” Comput. Biol. Med., 39(12), pp. 1083–1088. [CrossRef] [PubMed]
Sartori, M. , Reggiani, M. , Farina, D. , and Lloyd, D. G. , 2012, “ EMG-Driven Forward-Dynamic Estimation of Muscle Force and Joint Moment About Multiple Degrees of Freedom in the Human Lower Extremity,” PLoS One, 7(12), p. e52618. [CrossRef] [PubMed]
Ting, L. H. , and McKay, J. L. , 2007, “ Neuromechanics of Muscle Synergies for Posture and Movement,” Curr. Opin. Neurobiol., 17(6), pp. 622–628. [CrossRef] [PubMed]
Routson, R. L. , Kautz, S. A. , and Neptune, R. R. , 2014, “ Modular Organization Across Changing Task Demands in Healthy and Poststroke Gait,” Physiol. Rep., 2(6), p. e12055. [CrossRef] [PubMed]
Ivanenko, Y. P. , Cappellini, G. , Dominici, N. , Poppele, R. E. , and Lacquaniti, F. , 2005, “ Coordination of Locomotion With Voluntary Movements in Humans,” J. Neurosci., 25(31), pp. 7238–7253. [CrossRef] [PubMed]
Rodriguez, K. L. , Roemmich, R. T. , Cam, B. , Fregly, B. J. , and Hass, C. J. , 2013, “ Persons With Parkinson's Disease Exhibit Decreased Neuromuscular Complexity During Gait,” Clin. Neurophysiol., 124(7), pp. 1390–1397. [CrossRef] [PubMed]
Bianco, N. A. , Kinney, A. L. , and Fregly, B. J. , 2014, “ Predicting Unmeasured Muscle Excitations From Measured Muscle Synergies,” 7th World Congress of Biomechanics, Boston, MA, July 6–11.
Walter, J. P. , Kinney, A. L. , Banks, S. A. , D'Lima, D. D. , Besier, T. F. , Lloyd, D. G. , and Fregly, B. J. , 2014, “ Muscle Synergies May Improve Optimization Prediction of Knee Contact Forces During Walking,” ASME J. Biomech. Eng., 136(2), p. 021031. [CrossRef]
Fregly, B. J. , Besier, T. F. , Lloyd, D. G. , Delp, S. L. , Banks, S. A. , Pandy, M. G. , and D'Lima, D. D. , 2012, “ Grand Challenge Competition to Predict In Vivo Knee Loads,” J. Orthop. Res., 30(4), pp. 503–513. [CrossRef] [PubMed]
Bergmann, G. , Bender, A. , Graichen, F. , Dymke, J. , Rohlmann, A. , Trepczynski, A. , Heller, M. O. , and Kutzner, I. , 2014, “ Standardized Loads Acting in Knee Implants,” PLoS One, 9(1), p. e86035. [CrossRef] [PubMed]
D'Lima, D. D. , Townsend, C. P. , Arms, S. W. , Morris, B. A. , and Colwell, C. W. , 2005, “ An Implantable Telemetry Device to Measure Intra-Articular Tibial Forces,” J. Biomech., 38(2), pp. 299–304. [CrossRef] [PubMed]
Kristianslund, E. , Krosshaug, T. , and Van den Bogert, A. J. , 2012, “ Effect of Low Pass Filtering on Joint Moments From Inverse Dynamics: Implications for Injury Prevention,” J. Biomech., 45(4), pp. 666–671. [CrossRef] [PubMed]
He, J. , Levine, W. S. , and Loeb, G. E. , 1991, “ Feedback Gains for Correcting Small Perturbations to Standing Posture,” IEEE Trans. Automat. Contr., 36(3), pp. 322–332. [CrossRef]
Winters, J. M. , and Stark, L. , 1988, “ Estimated Mechanical Properties of Synergistic Muscles Involved in Movements of a Variety of Human Joints,” J. Biomech., 21(12), pp. 1027–1041. [CrossRef] [PubMed]
Lee, D. D. , and Seung, H. S. , 1999, “ Learning the Parts of Objects by Non-Negative Matrix Factorization,” Nature, 401(6755), pp. 788–791. [CrossRef] [PubMed]
Ting, L. , and Chvatal, S. , 2010, “ Decomposing Muscle Activity in Motor Tasks,” Motor Control: Theories, Experiments and Applications, F. Danion , and M. Latash , eds., Oxford University Press, New York.
Delp, S. L. , Anderson, F. C. , Arnold, A. S. , Loan, P. , Habib, A. , John, C. T. , Guendelman, E. , and Thelen, D. G. , 2007, “ OpenSim: Open-Source Software to Create and Analyze Dynamic Simulations of Movement,” IEEE Trans. Biomed. Eng., 54(11), pp. 1940–1950. [CrossRef] [PubMed]
Bei, Y. , and Fregly, B. J. , 2004, “ Multibody Dynamic Simulation of Knee Contact Mechanics,” Med. Eng. Phys., 26(9), pp. 777–789. [CrossRef] [PubMed]
Arnold, E. M. , Hamner, S. R. , Seth, A. , Millard, M. , and Delp, S. L. , 2013, “ How Muscle Fiber Lengths and Velocities Affect Muscle Force Generation as Humans Walk and Run at Different Speeds,” J. Exp. Biol., 216(Pt. 11), pp. 2150–2160. [CrossRef] [PubMed]
Campen, A. V. , Pipeleers, G. , De Groote, F. , Jonkers, I. , and De Schutter, J. , 2014, “ A New Method for Estimating Subject-Specific Muscle—Tendon Parameters of the Knee Joint Actuators: A Simulation Study,” Int. J. Numer. Method. Biomed. Eng., 30(10), pp. 969–987. [CrossRef] [PubMed]
Kaufman, K. R. , An, K. N. , Litchy, W. J. , and Chao, E. Y. S. , 1991, “ Physiological Prediction of Muscle Forces—I. Theoretical Formulation,” Neuroscience, 40(3), pp. 781–792. [CrossRef] [PubMed]
Arnold, E. , and Delp, S. , 2011, “ Fibre Operating Lengths of Human Lower Limb Muscles During Walking,” Philos. Trans. R. Soc. B, 366(1570), pp. 1530–1539. [CrossRef]
Rubenson, J. , Pires, N. J. , Loi, H. O. , Pinniger, G. J. , and Shannon, D. G. , 2012, “ On the Ascent: The Soleus Operating Length is Conserved to the Ascending Limb of the Force-Length Curve Across Gait Mechanics in Humans,” J. Exp. Biol., 215(Pt. 20), pp. 3539–3551. [CrossRef] [PubMed]
Kinney, A. L. , Besier, T. F. , D'Lima, D. D. , and Fregly, B. J. , 2013, “ Update on Grand Challenge Competition to Predict In Vivo Knee Loads,” ASME J. Biomech. Eng., 135(2), p. 021012. [CrossRef]
Manal, K. , and Buchanan, T. S. , 2013, “ An Electromyogram-Driven Musculoskeletal Model of the Knee to Predict In Vivo Joint Contact Forces During Normal and Novel Gait Patterns,” ASME J. Biomech. Eng., 135(2), p. 021014. [CrossRef]
Herzog, W. , Longino, D. , and Clark, A. , 2003, “ The Role of Muscles in Joint Adaptation and Degeneration,” Langenbecks Arch. Surg., 388(5), pp. 305–315. [CrossRef] [PubMed]
Buchanan, T. S. , Lloyd, D. G. , Manal, K. , and Besier, T. F. , 2005, “ Estimation of Muscle Forces and Joint Moments Using a Forward-Inverse Dynamics Model,” Med. Sci. Sport. Exercise, 37(11), pp. 1911–1916. [CrossRef]
Anderson, F. C. , and Pandy, M. G. , 2001, “ Static and Dynamic Optimization Solutions for Gait are Practically Equivalent,” J. Biomech., 34(2), pp. 153–161. [CrossRef] [PubMed]
Fraysse, F. , Dumas, R. , Cheze, L. , and Wang, X. , 2009, “ Comparison of Global and Joint-to-Joint Methods for Estimating the Hip Joint Load and the Muscle Forces During Walking,” J. Biomech., 42(14), pp. 2357–2362. [CrossRef] [PubMed]
Seth, A. , and Pandy, M. G. , 2007, “ A Neuromusculoskeletal Tracking Method for Estimating Individual Muscle Forces in Human Movement,” J. Biomech., 40(2), pp. 356–366. [CrossRef] [PubMed]
Besier, T. F. , Fredericson, M. , Gold, G. E. , Beaupré, G. S. , and Delp, S. L. , 2009, “ Knee Muscle Forces During Walking and Running in Patellofemoral Pain Patients and Pain-Free Controls,” J. Biomech., 42(7), pp. 898–905. [CrossRef] [PubMed]
Brandon, S. C. E. , Miller, R. H. , Thelen, D. G. , and Deluzio, K. J. , 2014, “ Selective Lateral Muscle Activation in Moderate Medial Knee Osteoarthritis Subjects Does Not Unload Medial Knee Condyle,” J. Biomech., 47(6), pp. 1409–1415. [CrossRef] [PubMed]
Kim, H. J. , Fernandez, J. W. , Akbarshahi, M. , Walter, J. P. , Fregly, B. J. , and Pandy, M. G. , 2009, “ Evaluation of Predicted Knee-Joint Muscle Forces During Gait Using an Instrumented Knee Implant.,” J. Orthop. Res., 27(10), pp. 1326–1331. [CrossRef] [PubMed]
Lin, Y.-C. , Walter, J. P. , Banks, S. A. , Pandy, M. G. , and Fregly, B. J. , 2010, “ Simultaneous Prediction of Muscle and Contact Forces in the Knee During Gait,” J. Biomech., 43(5), pp. 945–952. [CrossRef] [PubMed]
Ackland, D. C. , Lin, Y.-C. , and Pandy, M. G. , 2012, “ Sensitivity of Model Predictions of Muscle Function to Changes in Moment Arms and Muscle-Tendon Properties: A Monte-Carlo Analysis,” J. Biomech., 45(8), pp. 1463–1471. [CrossRef] [PubMed]
Redl, C. , Gfoehler, M. , and Pandy, M. G. , 2007, “ Sensitivity of Muscle Force Estimates to Variations in Muscle-Tendon Properties,” Hum. Mov. Sci., 26(2), pp. 306–319. [CrossRef] [PubMed]
Scovil, C. Y. , and Ronsky, J. L. , 2006, “ Sensitivity of a Hill-Based Muscle Model to Perturbations in Model Parameters,” J. Biomech., 39(11), pp. 2055–2063. [CrossRef] [PubMed]


Grahic Jump Location
Fig. 1

Block diagram of the two-step optimization formulation. asyn stands for activations reconstructed from synergy components, ma and madev for moment arm and moment arm deviations, respectively, sa for activation scale factors for muscles with experimental EMG data, SVmod for synergy vectors for muscles without experimental EMG data, l0M and sl0M for optimal fiber lengths and their scale factors, lsT and slsT for tendon slack lengths and their scale factors, a for model activations, ares for reserve activations, Fs for reserve actuator strength values (which are 0.5 Nm), athres for half-range of allowable activation variation (0.01 for muscles with associated experimental EMG data and 0.05 for all other muscles), f for muscle forces, and M for inverse dynamic moments. i is the muscle (44 muscles), j is the time frame (101 frames), and k is the tracked joint moment (six loads).

Grahic Jump Location
Fig. 2

Experimental knee contact forces and mean knee contact force predictions for approaches A and B. The gray area corresponds to the mean ± standard deviation for the experimental forces.

Grahic Jump Location
Fig. 3

Muscle forces for muscles with the greatest mean differences between approaches A and B. The plotted area corresponds to the mean ± standard deviation for all six gait cycles.

Grahic Jump Location
Fig. 4

Normalized muscle fiber lengths for muscles with the greatest differences in mean muscle forces between approaches A and B. The plotted area corresponds to the mean ± 1 standard deviation for all six gait cycles.

Grahic Jump Location
Fig. 5

Activations reconstructed from synergy components (activationSyn in solid lines) and model activations (activation in dashed lines) for muscles with associated experimental EMG data in one representative gait cycle. Asterisks (*) indicate statistically different r values between approaches A and B.



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In