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Research Papers

Electromyography-Driven Forward Dynamics Simulation to Estimate In Vivo Joint Contact Forces During Normal, Smooth, and Bouncy Gaits

[+] Author and Article Information
Swithin S. Razu

Department of Bioengineering,
University of Missouri,
801 Clark Hall,
Columbia, MO 65211-4250
e-mail: swithinr@health.missouri.edu

Trent M. Guess

Department of Physical Therapy,
University of Missouri,
801 Clark Hall,
Columbia, MO 65211-4250;
Department of Orthopaedic Surgery,
University of Missouri,
1100 Virginia Ave,
Columbia, MO 65201
e-mail: guesstr@health.missouri.edu

1Corresponding author.

Manuscript received June 8, 2017; final manuscript received October 25, 2017; published online May 18, 2018. Assoc. Editor: Tammy L. Haut Donahue.

J Biomech Eng 140(7), 071012 (May 18, 2018) (8 pages) Paper No: BIO-17-1248; doi: 10.1115/1.4038507 History: Received June 08, 2017; Revised October 25, 2017

Computational models that predict in vivo joint loading and muscle forces can potentially enhance and augment our knowledge of both typical and pathological gaits. To adopt such models into clinical applications, studies validating modeling predictions are essential. This study created a full-body musculoskeletal model using data from the “Sixth Grand Challenge Competition to Predict in vivo Knee Loads.” This model incorporates subject-specific geometries of the right leg in order to concurrently predict knee contact forces, ligament forces, muscle forces, and ground contact forces. The objectives of this paper are twofold: (1) to describe an electromyography (EMG)-driven modeling methodology to predict knee contact forces and (2) to validate model predictions by evaluating the model predictions against known values for a patient with an instrumented total knee replacement (TKR) for three distinctly different gait styles (normal, smooth, and bouncy gaits). The model integrates a subject-specific knee model onto a previously validated generic full-body musculoskeletal model. The combined model included six degrees-of-freedom (6DOF) patellofemoral and tibiofemoral joints, ligament forces, and deformable contact forces with viscous damping. The foot/shoe/floor interactions were modeled by incorporating shoe geometries to the feet. Contact between shoe segments and the floor surface was used to constrain the shoe segments. A novel EMG-driven feedforward with feedback trim motor control strategy was used to concurrently estimate muscle forces and knee contact forces from standard motion capture data collected on the individual subject. The predicted medial, lateral, and total tibiofemoral forces represented the overall measured magnitude and temporal patterns with good root-mean-squared errors (RMSEs) and Pearson's correlation (p2). The model accuracy was high: medial, lateral, and total tibiofemoral contact force RMSEs = 0.15, 0.14, 0.21 body weight (BW), and (0.92 < p2 < 0.96) for normal gait; RMSEs = 0.18 BW, 0.21 BW, 0.29 BW, and (0.81 < p2 < 0.93) for smooth gait; and RMSEs = 0.21 BW, 0.22 BW, 0.33 BW, and (0.86 < p2 < 0.95) for bouncy gait, respectively. Overall, the model captured the general shape, magnitude, and temporal patterns of the contact force profiles accurately. Potential applications of this proposed model include predictive biomechanics simulations, design of TKR components, soft tissue balancing, and surgical simulation.

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Figures

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Fig. 3

Three-dimensional model of the lower limb: (a) bony geometries including the wrapping surfaces for the pelvis, femur, and tibia, (b) wrapping surfaces for the medial and LG, and (c) via points for ankle plantar flexors

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Fig. 2

Shoe and foot model. The shoe geometry is divided into three rigid bodies: the shoe, shoe toes, and shoe tip. The foot geometry is divided into two rigid bodies: the foot body and foot toes. Deformable contacts are defined between the three shoe parts and the force plate.

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Fig. 1

The knee model used subject-specific bone and knee prosthetic component geometries including ligaments and the patellar tendon. The knee model was integrated into a generic full-body model which included 44 muscle-tendon actuators acting about the hip, knee, and ankle. The standing radiograph was used to confirm limb alignment in the coronal plane.

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Fig. 4

Feedforward with feedback control scheme for calculating muscle forces and joint contact forces. The feedforward muscle scheme incorporates experimental EMG in conjunction with musculotendon (activation and contraction) dynamics to produce feedforward muscle forces. The feedback muscle scheme uses the error between the current muscle length and the desired muscle length to produce feed-back trim muscle forces. The predicted muscle forces are the sum of the feedforward muscle forces and calculated feed-back trim muscle forces.

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Fig. 6

Comparison of normalized experimental EMG and predicted total and feedforward muscle forces for the muscles of BFLH, MG, GMAX, SM, GMED, TA, VL, and SO for the three versions of gait. Note: scale for muscle forces of VL and SO (bottom row) is greater than scale for other muscles.

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Fig. 5

Medial, lateral, and total tibiofemoral contact forces compared with in vivo measurements obtained during three modifications of gait. Contact force is scaled to bodyweight with 1 BW equal to 686 N.

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