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Research Papers

An Injury Risk Function for the Leg, Foot, and Ankle Exposed to Axial Impact Loading Using Force and Impulse

[+] Author and Article Information
Ann M. Bailey

Center for Applied Biomechanics,
University of Virginia,
4040 Lewis and Clark Drive,
Charlottesville, VA 22911
e-mail: amb9um@virginia.edu

Timothy L. McMurry

Department of Public Health Sciences,
School of Medicine,
University of Virginia,
P.O. Box 800717,
Charlottesville, VA 22908
e-mail: tlm6w@virginia.edu

Robert S. Salzar

Center for Applied Biomechanics,
University of Virginia,
4040 Lewis and Clark Drive,
Charlottesville, VA 22911
e-mail: rss2t@virginia.edu

Jeff R. Crandall

Center for Applied Biomechanics,
University of Virginia,
4040 Lewis and Clark Drive,
Charlottesville, VA 22911
e-mail: jrc2 h@virginia.edu

1Corresponding author.

Manuscript received February 18, 2018; final manuscript received November 4, 2018; published online December 12, 2018. Assoc. Editor: Brian D. Stemper.

J Biomech Eng 141(2), 021009 (Dec 12, 2018) (7 pages) Paper No: BIO-18-1093; doi: 10.1115/1.4042012 History: Received February 18, 2018; Revised November 04, 2018

Most injury risk functions (IRFs) for dynamic axial loading of the leg have been targeted toward automotive applications such as predicting injury caused by intrusion into the occupant compartment from frontal collisions. Recent focus on leg injuries in the military has led to questions about the applicability of these IRFs shorter duration, higher amplitude loading associated with underbody blast (UBB). To investigate these questions, data were collected from seven separate test series that subjected post-mortem human legs to axial impact. A force and impulse-based Weibull survival model was developed from these studies to estimate fracture risk. Specimen age was included as a covariate to reduce variance and improve survival model fit. The injury criterion estimated 50% risk of injury for a leg exposed to 13 N s of impulse at peak force and 8.07 kN of force for force durations less than and greater than half the natural period of the leg, respectively. A supplemental statistical analysis estimated that the proposed IRF improves injury prediction accuracy by more than 9% compared to the predictions from automobile-based risk functions developed for automotive intrusion. The proposed leg IRF not only improves injury prediction for higher rate conditions but also provides a single injury prediction tool for an expanded range of load durations ranging from 5 to 90 ms, which spans both automotive and military loading environments.

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Figures

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Fig. 1

Diagram reproduced based on von Gierke et al. [12], which demonstrates the theoretical dependence of injury threshold (deformation) on pulse shape, duration, and peak for various input pulses. Lines represent constant deformation response, which can be correlated with injury.

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Fig. 2

Comparison of average fracture time, plantar force at fracture, and impulse at fracture for the three test conditions used by Bailey et al. [15]. Note that the low condition did not produce fracture so peaks are reported.

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Fig. 3

Iso-compression plot for peak foot compression based on the peak foot compression from the numerical solution to dynamic model of the leg developed by Perry et al. [20] as a function of peak plantar force and impulse at peak force [19]

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Fig. 4

Peak plantar force versus time of peak plantar force (left) and impulse at peak plantar force versus force time-to-peak (right) for the combined data set. Injury and no injury tests are denoted by different symbols.

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Fig. 5

Contours of injury probability based on the force–impulse predictor variable. The top left contour plot shows results from the model with no covariates, and the remaining plots show the results of the model with age and body mass as a covariates for ages 25 and 45, and 78 and 100 kg.

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Fig. 6

Comparison of proposed force–impulse-based IRF to plantar (footplate) force IRFs in terms of AUC, Kruskal's gamma, Yule's Q, and accuracy

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