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Research Papers

Muscle Synergies May Improve Optimization Prediction of Knee Contact Forces During Walking

[+] Author and Article Information
Jonathan P. Walter

Department of Mechanical &
Aerospace Engineering,
University of Florida,
Gainesville, FL 32611

Allison L. Kinney, Scott A. Banks

Department of Mechanical &
Aerospace Engineering,
University of Florida,
Gainesville, FL 32611

Darryl D. D'Lima

Shiley Center for Orthopaedic
Research & Education,
Scripps Clinic,
La Jolla, CA 92037

Thor F. Besier

Auckland Bioengineering Institute &
Department of Engineering Science,
University of Auckland,
Auckland 1142, New Zealand

David G. Lloyd

Centre for Musculoskeletal Research,
Griffith Health Institute,
Griffith University,
Gold Coast Campus QLD 4222, Australia

Benjamin J. Fregly

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

1Corresponding author.

Contributed by the Bioengineering Division of ASME for publication in the Journal of Biomechanical Engineering. Manuscript received September 12, 2013; final manuscript received December 16, 2013; accepted manuscript posted January 7, 2014; published online February 5, 2014. Editor: Beth Winkelstein.

J Biomech Eng 136(2), 021031 (Feb 05, 2014) (9 pages) Paper No: BIO-13-1427; doi: 10.1115/1.4026428 History: Received September 12, 2013; Revised December 16, 2013; Accepted January 07, 2014

The ability to predict patient-specific joint contact and muscle forces accurately could improve the treatment of walking-related disorders. Muscle synergy analysis, which decomposes a large number of muscle electromyographic (EMG) signals into a small number of synergy control signals, could reduce the dimensionality and thus redundancy of the muscle and contact force prediction process. This study investigated whether use of subject-specific synergy controls can improve optimization prediction of knee contact forces during walking. To generate the predictions, we performed mixed dynamic muscle force optimizations (i.e., inverse skeletal dynamics with forward muscle activation and contraction dynamics) using data collected from a subject implanted with a force-measuring knee replacement. Twelve optimization problems (three cases with four subcases each) that minimized the sum of squares of muscle excitations were formulated to investigate how synergy controls affect knee contact force predictions. The three cases were: (1) Calibrate+Match where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously matched, (2) Precalibrate+Predict where experimental knee contact forces were predicted using precalibrated muscle model parameters values from the first case, and (3) Calibrate+Predict where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously predicted, all while matching inverse dynamic loads at the hip, knee, and ankle. The four subcases used either 44 independent controls or five synergy controls with and without EMG shape tracking. For the Calibrate+Match case, all four subcases closely reproduced the measured medial and lateral knee contact forces (R2 ≥ 0.94, root-mean-square (RMS) error < 66 N), indicating sufficient model fidelity for contact force prediction. For the Precalibrate+Predict and Calibrate+Predict cases, synergy controls yielded better contact force predictions (0.61 < R2 < 0.90, 83 N < RMS error < 161 N) than did independent controls (-0.15 < R2 < 0.79, 124 N < RMS error < 343 N) for corresponding subcases. For independent controls, contact force predictions improved when precalibrated model parameter values or EMG shape tracking was used. For synergy controls, contact force predictions were relatively insensitive to how model parameter values were calibrated, while EMG shape tracking made lateral (but not medial) contact force predictions worse. For the subject and optimization cost function analyzed in this study, use of subject-specific synergy controls improved the accuracy of knee contact force predictions, especially for lateral contact force when EMG shape tracking was omitted, and reduced prediction sensitivity to uncertainties in muscle model parameter values.

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References

Figures

Grahic Jump Location
Fig. 2

Root-mean-square errors and R2 values for medial, lateral, and total knee contact force predictions from all optimizations. For the Calibrate+Match cases, muscle model parameter values were calibrated to match experimental knee contact forces. For the Precalibrate+Predict cases, experimental knee contact forces were predicted using precalibrated muscle model parameter values from the first case. For the Calibrate+Predict cases, muscle model parameter values were calibrated while knee contact forces were predicted simultaneously. Subcases that used synergy controls are indicated by the label Syn, while subcases that used independent controls are indicated by the label Ind. Subcases that tracked EMG shapes are indicated by the additional label EMG.

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Fig. 3

Experimental and predicted medial, lateral, and total knee contact forces for all optimizations. See caption of Fig. 2 for descriptions of cases and subcases.

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Fig. 1

Normalized neural command curves calculated from 13 processed experimental EMG signals. These five neural commands were able to account for 95% of the variability in all experimental EMG curves and were used to construct 44 simulated muscle excitation signals for muscle force optimizations using synergy controls.

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Fig. 4

Average %VAF and R2 values for EMG shape predictions from all optimizations. Each bar represents the average and each whisker the standard deviation for all muscles with experimental EMG data. See caption for Fig. 2 for descriptions of cases and subcases.

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Fig. 5

Sample muscle forces for Calibrate+Predict case for muscles (lateral gastrocnemius – LatGas, medial gastrocnemius – MedGas, rectus femoris – RF, and vastus medialis – VasMed) with large contributions to knee contact forces. See caption for Fig. 2 of descriptions of cases and subcases.

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