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Research Papers

Co-Simulation of Neuromuscular Dynamics and Knee Mechanics During Human Walking

[+] Author and Article Information
Darryl G. Thelen

Department of Mechanical Engineering,
University of Wisconsin-Madison,
Madison, WI 53706;
Department of Biomedical Engineering,
University of Wisconsin-Madison,
Madison, WI 53706;
Department of Orthopedics and Rehabilitation,
University of Wisconsin-Madison,
Madison, WI 53706
e-mail: thelen@engr.wisc.edu

Kwang Won Choi

Department of Mechanical Engineering,
University of Wisconsin-Madison,
Madison, WI 53706

Anne M. Schmitz

Department of Biomedical Engineering,
University of Wisconsin-Madison,
Madison, WI 53706

See https://simtk.org/home/kneeloads for the competition data.

1Corresponding author.

Contributed by the Bioengineering Division of ASME for publication in the Journal of Biomechanical Engineering. Manuscript received September 6, 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), 021033 (Feb 05, 2014) (8 pages) Paper No: BIO-13-1414; doi: 10.1115/1.4026358 History: Received September 06, 2013; Revised December 19, 2013; Accepted December 26, 2013

This study introduces a framework for co-simulating neuromuscular dynamics and knee joint mechanics during gait. A knee model was developed that included 17 ligament bundles and a representation of the distributed contact between a femoral component and tibial insert surface. The knee was incorporated into a forward dynamics musculoskeletal model of the lower extremity. A computed muscle control algorithm was then used to modulate the muscle excitations to drive the model to closely track measured hip, knee, and ankle angle trajectories of a subject walking overground with an instrumented knee replacement. The resulting simulations predicted the muscle forces, ligament forces, secondary knee kinematics, and tibiofemoral contact loads. Model-predicted tibiofemoral contact forces were of comparable magnitudes to experimental measurements, with peak medial (1.95 body weight (BW)) and total (2.76 BW) contact forces within 4–17% of measured values. Average root-mean-square errors over a gait cycle were 0.26, 0.42, and 0.51 BW for the medial, lateral, and total contact forces, respectively. The model was subsequently used to predict variations in joint contact pressure that could arise by altering the frontal plane joint alignment. Small variations (±2 deg) in the alignment of the femoral component and tibial insert did not substantially affect the location of contact pressure, but did alter the medio-lateral distribution of load and internal tibia rotation in swing. Thus, the computational framework can be used to virtually assess the coupled influence of both physiological and design factors on in vivo joint mechanics and performance.

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Figures

Grahic Jump Location
Fig. 1

The three body knee mechanics model included 17 ligament bundles acting about the tibiofemoral and patellofemoral joints. Contact pressure between the femoral component and tibial insert was computed via an elastic foundation model. Ligament abbreviations are given in Table 1.

Grahic Jump Location
Fig. 2

A computed muscle control (CMC) algorithm was used to modulate the lower limb muscle excitations such that the simulation closely tracked the measured hip, knee, and ankle angles. At every time step, the tibia, patella, and femur positions were used to ascertain the tibiofemoral contact and ligament forces. These forces were then applied within the forward dynamic simulation of the neuromusculoskeletal model.

Grahic Jump Location
Fig. 3

Comparison of average electromyographic (EMG) data with simulated muscle excitations, activations, and forces over a gait cycle. Reasonably good temporal agreement is seen for the vastus lateralis, medial gastrocnemius, soleus, and tibialis anterior. Normal bursts of hamstring activity (semitendinosus, biceps femoris long) in late swing and early stance are also predicted, though the subject exhibited greater medial hamstring EMG activity throughout the gait cycle. Rectus femoris EMG activity near toe-off is slightly lower than that used in the model to initiate swing limb motion between 50% and 60% of the gait cycle. Simulated posterior cruciate and collateral ligament forces were greatest in mid-swing.

Grahic Jump Location
Fig. 4

Model-predicted medial, lateral, and total tibofemoral contact forces (expressed in units of body weight (BW)) over five experimental walking cycles. Experimentally measured contact forces represent the mean (+/-1 s.d.) over the same five repeat walking cycles. Peak lateral contact forces are of comparable magnitude to experimental forces in late stance, but the model predicts greater lateral contact forces in early stance (0–10%) and the first half of swing (60–80%) than was measured.

Grahic Jump Location
Fig. 5

Frontal plane alignment of the joint replacement substantially altered tibia rotation in swing and early stance, but had little effect on rotation when the limb was loaded in mid- and terminal stance. A more valgus joint replacement alignment induced greater knee abduction, lower medial contact forces, and higher lateral contact forces throughout stance.

Grahic Jump Location
Fig. 6

Predicted contact pressures on the tibial insert at the time of heel strike and the first and second peaks of the tibiofemoral contact loading. Greater valgus alignment of the joint replacement resulted in a more posteriorly loaded lateral compartment at heel strike. During stance, the location of the peak pressures did not vary with alignment, but more even pressure distribution across the medial and lateral compartments is predicted in the valgus alignment.

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