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Research Papers

Concurrent Prediction of Muscle and Tibiofemoral Contact Forces During Treadmill Gait

[+] Author and Article Information
Trent M. Guess

Associate Professor
Department of Physical Therapy,
Department of Orthopaedic Surgery,
University of Missouri,
801 Clark Hall,
Columbia, MO 65211-4250
e-mail: guesstr@missouri.edu

Antonis P. Stylianou

Research Associate
Department of Civil & Mechanical Engineering,
University of Missouri–Kansas City,
318 Robert H. Flarsheim Hall,
5110 Rockhill Road,
Kansas City, MO 64110
e-mail: stylianoua@umkc.edu

Mohammad Kia

Post-Doctoral Research Fellow
Department of Biomechanics,
Hospital for Special Surgery,
535 East 70th Street,
New York, NY 10021
e-mail: kiam@hss.edu

1Corresponding author.

Contributed by the Bioengineering Division of ASME for publication in the Journal of Biomechanical Engineering. Manuscript received August 31, 2013; final manuscript received December 19, 2013; accepted manuscript posted December 26, 2013; published online February 5, 2014. Editor: Beth Winkelstein.

J Biomech Eng 136(2), 021032 (Feb 05, 2014) (9 pages) Paper No: BIO-13-1400; doi: 10.1115/1.4026359 History: Received August 31, 2013; Revised December 19, 2013; Accepted December 26, 2013

Detailed knowledge of knee kinematics and dynamic loading is essential for improving the design and outcomes of surgical procedures, tissue engineering applications, prosthetics design, and rehabilitation. This study used publicly available data provided by the “Grand Challenge Competition to Predict in-vivo Knee Loads” for the 2013 American Society of Mechanical Engineers Summer Bioengineering Conference (Fregly et al., 2012, “Grand Challenge Competition to Predict in vivo Knee Loads,” J. Orthop. Res., 30, pp. 503–513) to develop a full body, musculoskeletal model with subject specific right leg geometries that can concurrently predict muscle forces, ligament forces, and knee and ground contact forces. The model includes representation of foot/floor interactions and predicted tibiofemoral joint loads were compared to measured tibial loads for two different cycles of treadmill gait. The model used anthropometric data (height and weight) to scale the joint center locations and mass properties of a generic model and then used subject bone geometries to more accurately position the hip and ankle. The musculoskeletal model included 44 muscles on the right leg, and subject specific geometries were used to create a 12 degrees-of-freedom anatomical right knee that included both patellofemoral and tibiofemoral articulations. Tibiofemoral motion was constrained by deformable contacts defined between the tibial insert and femoral component geometries and by ligaments. Patellofemoral motion was constrained by contact between the patellar button and femoral component geometries and the patellar tendon. Shoe geometries were added to the feet, and shoe motion was constrained by contact between three shoe segments per foot and the treadmill surface. Six-axis springs constrained motion between the feet and shoe segments. Experimental motion capture data provided input to an inverse kinematics stage, and the final forward dynamics simulations tracked joint angle errors for the left leg and upper body and tracked muscle length errors for the right leg. The one cycle RMS errors between the predicted and measured tibia contact were 178 N and 168 N for the medial and lateral sides for the first gait cycle and 209 N and 228 N for the medial and lateral sides for the faster second gait cycle. One cycle RMS errors between predicted and measured ground reaction forces were 12 N, 13 N, and 65 N in the anterior-posterior, medial-lateral, and vertical directions for the first gait cycle and 43 N, 15 N, and 96 N in the anterior-posterior, medial-lateral, and vertical directions for the second gait cycle.

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References

Andriacchi, T. P., Mundermann, A., Smith, R. L., Alexander, E. J., Dyrby, C. O., and Koo, S., 2004, “A Framework for the in vivo Pathomechanics of Osteoarthritis at the Knee,” Ann. Biomed. Eng., 32, pp. 447–457. [CrossRef] [PubMed]
WimmerM. A., and Andriacchi, T. P., 1997, “Tractive Forces During Rolling Motion of the Knee: Implications for Wear in Total Knee Replacement,” J. Biomech., 30, pp. 131–137,. [CrossRef] [PubMed]
SathasivamS., and Walker, P. S., 1998, “Computer Model to Predict Subsurface Damage in Tibial Inserts of Total Knees,” J. Orthop. Res., 16, pp. 564–571. [CrossRef] [PubMed]
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, pp. 503–513. [CrossRef] [PubMed]
Mündermann, A., Dyrby, C. O., D'Lima, D. D., Colwell, C. W., and Andriacchi, T. P., 2008, “In vivo Knee Loading Characteristics During Activities of Daily Living as Measured by an Instrumented Total Knee Replacement,” J. Orthop. Res., 26, pp. 1167–1172. [CrossRef] [PubMed]
Heinlein, B., Kutzner, I., Graichen, F., Bender, A., Rohlmann, A., Halder, A. M., Beier, A., and Bergmann, G., 2009 “ESB Clinical Biomechanics Award 2008: Complete Data of Total Knee Replacement Loading for Level Walking and Stair Climbing Measured in vivo With a Follow-Up of 6-10 Months,” Clin. Biomech. (Bristol, Avon), 24, pp. 315–326. [CrossRef] [PubMed]
Kutzner, I., Heinlein, B., Graichen, F., Bender, A., Rohlmann, A., Halder, A., Beier, A., and Bergmann, G., 2010, “Loading of the Knee Joint During Activities of Daily Living Measured in vivo in Five Subjects,” J. Biomech., 43, pp. 2164–2173. [CrossRef] [PubMed]
Taylor, W. R., Heller, M. O., Bergmann, G., and Duda, G. N., 2004, “Tibio-Femoral Loading During Human Gait and Stair Climbing,” J. Orthop. Res., 22, pp. 625–632. [CrossRef] [PubMed]
Anderson, F. C., and Pandy, M. G., 2001, “Dynamic Optimization of Human Walking,” ASME J. Biomech. Eng., 123(5), pp. 381–390. [CrossRef]
Piazza, S. J., 2006, “Muscle-Driven Forward Dynamic Simulations for the Study of Normal and Pathological Gait,” J. Neuroeng. Rehabil., 3, Paper No. 5. [CrossRef]
Piazza, S. J., and Delp, S. L., 2001, “Three-Dimensional Dynamic Simulation of Total Knee Replacement Motion During a Step-Up Task,” ASME J. Biomech. Eng., 123(6), pp. 599–606. [CrossRef]
Thelen, D. G., and Anderson, F. C., 2006, “Using Computed Muscle Control to Generate Forward Dynamic Simulations of Human Walking From Experimental Data,” J. Biomech., 39, pp. 1107–1115. [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, 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, pp. 945–952. [CrossRef] [PubMed]
Schipplein, O. D., and Andriacchi, T. P., 1991, “Interaction Between Active and Passive Knee Stabilizers During Level Walking,” J. Orthop. Res., 9, pp. 113–119. [CrossRef] [PubMed]
Lundberg, H. J., Foucher, K. C., and Wimmer, M. A., 2009, “A Parametric Approach to Numerical Modeling of TKR Contact Forces,” J. Biomech., 42, pp. 541–545. [CrossRef] [PubMed]
Shelburne, K. B., Torry, M. R., and Pandy, M. G., 2005, “Muscle, Ligament, and Joint-Contact Forces at the Knee During Walking,” Med. Sci. Sports Exerc., 37, pp. 1948–1956. [CrossRef] [PubMed]
Shelburne, K. B., Torry, M. R., and Pandy, M. G., 2006, “Contributions of Muscles, Ligaments, and the Ground-Reaction Force to Tibiofemoral Joint Loading During Normal Gait,” J. Orthop. Res., 24, pp. 1983–1990. [CrossRef] [PubMed]
D'Lima, D. D., Patil, S., Steklov, N., Slamin, J. E., and Colwell, C. W., Jr., 2005“The Chitranjan Ranawat Award: in vivo Knee Forces After Total Knee Arthroplasty,”Clin. Orthop. Relat. Res., 440, pp. 45–49. [CrossRef] [PubMed]
Delp, S. L., and Loan, J. P., 1995, “A Graphics-Based Software System to Develop and Analyze Models of Musculoskeletal Structures,” Comput. Biol. Med., 25, pp. 21–34. [CrossRef] [PubMed]
Machado, M., Moreira, P., Flores, P., and Lankarani, H., 2012, “Compliant Contact Force Models in Multibody Dynamics: Evolution of the Hertz Contact Theory,” Mech. Mach. Theory, 53, pp. 99–121. [CrossRef]
Guess, T. M., and Maletsky, L. P., 2005, “Computational Modelling of a Total Knee Prosthetic Loaded in a Dynamic Knee Simulator,” Med. Eng. Phys., 27, pp. 357–367. [CrossRef] [PubMed]
Blankevoort, L., Huiskes, R., and de Lange, A., 1991, “Recruitment of Knee Joint Ligaments,” ASME J. Biomech. Eng., 113(1), pp. 94–103. [CrossRef]
Wismans, J., Veldpaus, F., Janssen, J., Huson, A., and Struben, P., 1980, “A Three-Dimensional Mathematical Model of the Knee-Joint,” J. Biomech., 13, pp. 677–685. [CrossRef] [PubMed]
Guess, T. M., Liu, H., Bhashyam, S., and Thiagarajan, G., 2011, “A Multibody Knee Model With Discrete Cartilage Prediction of Tibio-Femoral Contact Mechanics,” Comput. Methods Biomech. Biomed. Eng., 16, pp. 256–270. [CrossRef]
Guess, T. M., Thiagarajan, G., Kia, M., and Mishra, M., 2010, “A Subject Specific Multibody Model of the Knee With Menisci,” Med. Eng. Phys., 32, pp. 505–515. [CrossRef] [PubMed]
Guess, T. M., and Stylianou, A., 2012, “Simulation of Anterior Cruciate Ligament Deficiency in a Musculoskeletal Model With Anatomical Knees,” Open Biomed. Eng. J., 6, pp. 23–32. [PubMed]
Nigg, B. M., Macintosh, B. R., and Mester, J., 2000, Biomechanics and Biology of Movement, Human Kinetics, Champaign, IL.
Schumacher, G. H., and Wolff, E., 1966, “Dry Weight and Physiological Cross Section of Human Skeletal Muscles. I. Dry Weight,” Anat. Anz., 118, pp. 317–330. [PubMed]
Schumacher, G. H., and Wolff, E., 1966, “Dry Weight and Physiological Cross Section of Human Skeletal Muscles. II. Physiological Cross Section,” Anat. Anz., 119, pp. 259–269. [PubMed]
Schumacher, G. H., and Wolff, E., 1966, “Dry Weight and Physiological Cross Section of Human Skeletal Muscles. 3. Relationship Between Dry Weight and Physiological Cross Section,” Anat. Anz., 119, pp. 270–283. [PubMed]
Neptune, R. R., Wright, I. C., and Van Den Bogert, A. J., 2000, “A Method for Numerical Simulation of Single Limb Ground Contact Events: Application to Heel-Toe Running,” Comput. Methods Biomech. Biomed. Eng., 3, pp. 321–334. [CrossRef]

Figures

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Fig. 1

Sagittal plane (a) and (c) and frontal plane (b) view of the right knee. Also shown are the contact patches on the medial and lateral side of the tibia insert during a forward dynamics simulation (d). The small spheres denote the location of muscle via points. The large spheres denote the location of motion capture markers associated with the body segments.

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Fig. 2

Shoe Model. The shoe geometry is separated into three rigid bodies: the shoe, shoe toes, and shoe tip. The shoe is connected to the foot via a six-axis spring, the shoe toes are connected to the toes via a six-axis spring, and the shoe tip is connected to the shoe toes through a six-axis spring. The foot and toe body segments are connected through a revolute joint (metatarsophalangeal joint). Deformable contacts are defined between the three shoe parts and the treadmill force plate.

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Fig. 3

Musculoskeletal simulation of treadmill gait with a right total knee replacement. The right leg is driven by muscle forces and the upper body and left leg are driven by joint torques. The arrows represent forces (contact, muscle, ligament, spring) calculated during the forward dynamics simulation.

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Fig. 4

Feedback control scheme for calculating muscle force. The Simulink “saturation” limits muscle forces such that a muscle can only pull and not push.

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Fig. 5

Normalized muscle forces during the forward dynamics simulation of the accelerating gait cycle for the global PID values: PID 50 (P = 50, I = 5, and D = 0.0005), PID 100 (P = 100, I = 10, and D = 0.001), and PID 300 (P = 300, I = 30, and D = 0.003). Muscle forces are normalized to the maximum force that can be produced by each muscle as determined by its PCSA.

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Fig. 6

Measured and predicted lateral (a) and medial (b) contact forces over the accelerating gait cycle for global PID values of 50, 100, and 300

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Fig. 7

Measured eKnee and model predictions for the accelerating gait cycle (lateral (a), medial (b)) and fast gait cycle (lateral (c), medial (d)). Global PID values equal 50.

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Fig. 8

Measured and predicted ground reaction forces for the right leg during the accelerating (a) and fast (b) gait cycles. Global PID values equal 50.

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Fig. 9

Right leg ankle and hip rotations for the accelerating and fast gait cycles. Shown are the inverse kinematics rotations and the forward dynamics rotations for the passive joints of the muscle force driven right leg. For the hip, the x-axis corresponds to flexion/extension, the y-axis corresponds to internal/external rotation, and z-axis rotations correspond to abduction/adduction. For the ankle, the x-axis corresponds to plantarflexion/dorsiflexion, the y-axis corresponds to internal/external rotation, and z-axis rotations correspond to inversion/eversion. Global PID values equal 50.

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Fig. 10

Normalized experimental EMG and normalized predicted muscle forces for the accelerating and fast gait cycles. Muscle forces are normalized to the maximum force that can be produced by each muscle as determined by its PCSA. Global PID values equal 50.

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