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Research Papers

An Electromyogram-Driven Musculoskeletal Model of the Knee to Predict in Vivo Joint Contact Forces During Normal and Novel Gait Patterns

[+] Author and Article Information
Kurt Manal

e-mail: manal@udel.edu

Thomas S. Buchanan

Delaware Rehabilitation Institute,
Department of Mechanical Engineering,
University of Delaware,
Newark, DE 19716

1Corresponding author.

Contributed by the Bioengineering Division of ASME for publication in the JOURNAL OF BIOMECHANICAL ENGINEERING. Manuscript received October 19, 2012; final manuscript received January 16, 2013; accepted manuscript posted January 18, 2013; published online February 7, 2013. Editor: Beth Winkelstein.

J Biomech Eng 135(2), 021014 (Feb 07, 2013) (7 pages) Paper No: BIO-12-1498; doi: 10.1115/1.4023457 History: Received October 19, 2012; Revised January 16, 2013; Accepted January 18, 2013

Computational models that predict internal joint forces have the potential to enhance our understanding of normal and pathological movement. Validation studies of modeling results are necessary if such models are to be adopted by clinicians to complement patient treatment and rehabilitation. The purposes of this paper are: (1) to describe an electromyogram (EMG)-driven modeling approach to predict knee joint contact forces, and (2) to evaluate the accuracy of model predictions for two distinctly different gait patterns (normal walking and medial thrust gait) against known values for a patient with a force recording knee prosthesis. Blinded model predictions and revised model estimates for knee joint contact forces are reported for our entry in the 2012 Grand Challenge to predict in vivo knee loads. The EMG-driven model correctly predicted that medial compartment contact force for the medial thrust gait increased despite the decrease in knee adduction moment. Model accuracy was high: the difference in peak loading was less than 0.01 bodyweight (BW) with an R2 = 0.92. The model also predicted lateral loading for the normal walking trial with good accuracy exhibiting a peak loading difference of 0.04 BW and an R2 = 0.44. Overall, the EMG-driven model captured the general shape and timing of the contact force profiles and with accurate input data the model estimated joint contact forces with sufficient accuracy to enhance the interpretation of joint loading beyond what is possible from data obtained from standard motion capture studies.

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References

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Figures

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Fig. 1

Transformation of raw EMG to muscle activation a(t) as an input to the EMG-driven model (from [18])

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Fig. 2

Schematic of a Hill-type muscle fiber. The musculotendon unit (shown on the left) has a muscle fiber in series with tendon. The musculotendon length lmt is the sum of the fiber length lm, adjusted for pennation angle Ø, and the tendon length lt. The muscle fiber shown on the right is comprised of an active contractile element in parallel with a passive elastic element. Fam is the force developed by active mechanisms (i.e., length-tension and force-velocity relationships). Fpm represents the passive force contribution when the muscle fiber is at a length beyond optimal (i.e., passive portion of the length-tension curve). The active and passive forces sum to yield the fiber force Fm, and when adjusted for pennation angle is equivalent to the force acting through the tendon Ft.

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Fig. 3

EMG for muscle m at time t was transformed into muscle activation a to activate a Hill-type muscle model (muscle contraction dynamics). The force F for each muscle was then multiplied by its sagittal plane moment arm r according to the musculoskeletal geometry which is dependent on the joint kinematics for the particular trial. Individual muscle moments are then summed at each point in time to obtain a model estimated sagittal plane knee moment. The knee moment was also calculated using inverse dynamics from video-based motion data and ground reaction forces. EMG-driven model parameters including activation coefficients, optimal fiber length (OFL), resting tendon length (RTL), and the maximum isometric force for each muscle were adjusted iteratively to minimize the sum-squared difference between the model estimated moment and the moment computed from inverse dynamics. The process of optimally adjusting model parameters is depicted by the gray shaded boxes and dashed arrows.

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Fig. 4

Schematic of the moment balancing algorithm to compute medial contact force (FMC). The external adduction moment about the lateral condyle MexternalLC must be balanced by a moment generated by muscles MmodelLC and an unknown contact force FMC acting at distance d equal to one half the width of the tibial plateau. riLC is the moment arm for muscle i relative to the lateral condyle and Fi is the force generated by the muscle.

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Fig. 5

The dark lines represent the EMG-driven model's prediction of joint contact force and the gray lines are the forces measured by the instrumented knee implant (eTibia). The model captured the general shape and timing of the contact force profiles with good predictions of medial contact for the medial thrust gait and lateral contact for normal walking. The largest difference between the model predicted and measured force was for the lateral compartment during medial thrust gait.

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Fig. 6

The dark lines represent the revised force estimates for the EMG-driven model and the gray lines are the forces measured by the instrument ted knee implant (eTibia). The revised model captured the general shape and timing of the contact force profiles with good agreement in peak values. The largest difference between the model predicted and measured force was 0.13 BW for the lateral compartment during medial thrust gait. All other difference in peak values were less than 0.05 BW.

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