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Research Papers

Predictive Neuromuscular Fatigue of the Lower Extremity Utilizing Computer Modeling

[+] Author and Article Information
Michael A. Samaan

Department of Mechanical
and Aerospace Engineering,
Old Dominion University,
Norfolk, VA 23529;
Department of Radiology
and Biomedical Imaging,
University of California–San Francisco,
San Francisco, CA 94107
e-mail: michael.samaan@ucsf.edu

Joshua T. Weinhandl

Department of Kinesiology,
Recreation and Sports Studies,
The University of Tennessee,
Knoxville, TN 37996
e-mail: jweinhan@utk.edu

Steven A. Hans

Department of Mechanical
and Aerospace Engineering,
Old Dominion University,
Norfolk, VA 23529
e-mail: shans001@odu.edu

Sebastian Y. Bawab

Department of Mechanical
and Aerospace Engineering,
Old Dominion University,
Norfolk, VA 23529
e-mail: sbawab@odu.edu

Stacie I. Ringleb

Department of Mechanical
and Aerospace Engineering,
Old Dominion University,
Norfolk, VA 23529
e-mail: sringleb@odu.edu

1Corresponding author.

Manuscript received December 16, 2014; final manuscript received November 7, 2015; published online December 8, 2015. Assoc. Editor: Silvia Blemker.

J Biomech Eng 138(1), 011008 (Dec 08, 2015) (10 pages) Paper No: BIO-14-1632; doi: 10.1115/1.4032071 History: Received December 16, 2014; Revised November 07, 2015

This paper studies the modeling of lower extremity muscle forces and their correlation to neuromuscular fatigue. Two analytical fatigue models were combined with a musculoskeletal model to estimate the effects of hamstrings fatigue on lower extremity muscle forces during a side step cut. One of the fatigue models (Tang) used subject-specific knee flexor muscle fatigue and recovery data while the second model (Xia) used previously established fatigue and recovery parameters. Both fatigue models were able to predict hamstrings fatigue within 20% of the experimental data, with the semimembranosus and semitendinosus muscles demonstrating the largest (11%) and smallest (1%) differences, respectively. In addition, various hamstrings fatigue levels (10–90%) on lower extremity muscle force production were assessed using one of the analytical fatigue models. As hamstrings fatigue levels increased, the quadriceps muscle forces decreased by 21% (p < 0.01), while gastrocnemius muscle forces increased by 36% (p < 0.01). The results of this study validate the use of two analytical fatigue models in determining the effects of neuromuscular fatigue during a side step cut, and therefore, this model can be used to assess fatigue effects on risk of lower extremity injury during athletic maneuvers. Understanding the effects of fatigue on muscle force production may provide insight on muscle group compensations that may lead to altered lower extremity motion patterns as seen in noncontact anterior cruciate ligament (ACL) injuries.

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References

Figures

Grahic Jump Location
Fig. 1

The workflow shown here summarizes the processes used in opensim to produce the musculoskeletal simulations that were integrated with both the Tang et al. [16] and Xia and Frey Law [4] fatigue models

Grahic Jump Location
Fig. 2

The hamstrings fatigue protocol was implemented using a portable fixed dynamometer. This device consists of a load cell attached to a wall on one end and the other end was attached to the participant using an ankle strap.

Grahic Jump Location
Fig. 3

A participant’s hamstrings fatigue (top) and recovery (bottom) curves, normalized by MVC, are displayed. The dashed lines in the fatigue and recovery plots represent the failure criteria of 25% and recovery criteria of 80% MVC, respectively. The solid lines represent the exponential approximation of the fatigue and recovery data. In addition, the exponential equations that approximate the participant’s hamstrings fatigue and recovery are displayed.

Grahic Jump Location
Fig. 4

Fatigued hamstrings total muscle force, normalized by BW, predicted by the postfatigue CMC data (solid), the Tang (dotted) and Xia (dashed) fatigue models. The 95% confidence interval of the postfatigue CMC muscle force is shown as well.

Grahic Jump Location
Fig. 5

The Xia and Frey Law [4] fatigue model was used to test the effect of various levels of hamstrings fatigue on total quadriceps muscle force. The group average total quadriceps muscle force, normalized by BW, from 20 ms prior to and 50 ms after initial contact during the side step cut is displayed.

Grahic Jump Location
Fig. 6

The Xia and Frey Law [4] fatigue model was used to test the effect of various levels of hamstrings fatigue on muscle force production. The group average total hamstrings muscle force, normalized by BW, from 20 ms prior to and 50 ms after initial contact during the side step cut is displayed.

Grahic Jump Location
Fig. 7

The Xia and Frey Law [4] fatigue model was used to test the effect of various levels of hamstrings fatigue on total gastrocnemius muscle force. The group average total gastrocnemius muscle force, normalized by BW, from 20 ms prior to and 50 ms after initial contact during the side step cut is displayed.

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