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

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Ann M. Bailey ◽  
Timothy L. McMurry ◽  
Robert S. Salzar ◽  
Jeff R. Crandall

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.

2021 ◽  
Author(s):  
Madelen Fahlstedt ◽  
Shiyang Meng ◽  
Svein Kleiven

Finite element head models are a tool to better understand brain injury mechanisms. Many of the models use strain as output but with different percentile values such as 100th, 95th, 90th, and 50th percentiles. Some use the element value, whereas other use the nodal average value for the element. Little is known how strain post-processing is affecting the injury predictions and evaluation of different prevention systems. The objective of this study was to evaluate the influence of strain output on injury prediction and ranking. Two models with different mesh densities were evaluated (KTH Royal Institute of Technology head model and the Total Human Models for Safety (THUMS)). Pulses from reconstructions of American football impacts with and without a diagnosis of mild traumatic brain injury were applied to the models. The value for 100th, 99th, 95th, 90th, and 50th percentile for element and nodal averaged element strain was evaluated based on peak values, injury risk functions, injury predictability, correlation in ranking, and linear correlation. The injury risk functions were affected by the post-processing of the strain, especially the 100th percentile element value stood out. Meanwhile, the area under the curve (AUC) value was less affected, as well as the correlation in ranking (Kendall's tau 0.71-1.00) and the linear correlation (Pearson's r2 0.72-1.00). With the results presented in this study, it is important to stress that the same post-processed strain should be used for injury predictions as the one used to develop the risk function.


2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Ishan Acharya ◽  
John T. Van Tuyl ◽  
Julia de Lange ◽  
Cheryl E. Quenneville

Lower leg injuries commonly occur in frontal automobile collisions, and are associated with high disability rates. Accurate methods to predict these injuries must be developed to facilitate the testing and improvement of vehicle safety systems. Anthropomorphic test devices (ATDs) are often used to assess injury risk by mimicking the behavior of the human body in a crash while recording data from sensors at discrete locations, which are then compared to established safety limits developed by cadaveric testing. Due to the difference in compliance of cadaveric and ATD legs, the force dissipating characteristics of footwear, and the lack of direct measurement of injury risk to the foot and ankle, a novel instrumented insole was developed that could be applied equally to all specimens both during injury limit generation and during safety evaluation tests. An array of piezoresistive sensors were calibrated over a range of speeds using a pneumatic impacting apparatus, and then applied to the insole of a boot. The boot was subsequently tested and compared to loads measured using ankle and toe load cells in an ATD, and found to have an average error of 10%. The sensors also provided useful information regarding the force distribution across the sole of the foot during an impact, which may be used to develop regional injury criteria. This work has furthered the understanding of lower leg injury prediction and developed a tool that may be useful in developing accurate injury criteria in the future for the foot and lower leg.


2003 ◽  
Vol 35 (6) ◽  
pp. 869-875 ◽  
Author(s):  
Stefan M. Duma ◽  
Brian M. Boggess ◽  
Jeff R. Crandall ◽  
Conor B. MacMahon

2018 ◽  
Vol 19 (5) ◽  
pp. 518-522 ◽  
Author(s):  
Nino Andricevic ◽  
Mirko Junge ◽  
Jonas Krampe
Keyword(s):  

2011 ◽  
Vol 40 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Steven Rowson ◽  
Stefan M. Duma ◽  
Jonathan G. Beckwith ◽  
Jeffrey J. Chu ◽  
Richard M. Greenwald ◽  
...  

2018 ◽  
Vol 31 (0) ◽  
Author(s):  
Priscila dos Santos Bunn ◽  
Glória de Paula Silva ◽  
Elirez Bezerra da Silva

Abstract Introduction: The Deep Squat Test has been applied in pre-season evaluations of sports teams and in military courses to predict the risk of musculoskeletal injuries. Objective: To evaluate the association of DS performance and the risk of musculoskeletal injuries. Methods: In this systematic review, a search without language or time filters was carried out in MEDLINE, SciELO, SCOPUS, SPORTDiscuss, CINAHL and BVS databases with the following title words: injury prediction, injury risk and deep squat in December 2016. Participants' profile, sample size, classification of musculoskeletal injuries, follow-up time, study design and results were extracted from the studies. Bias risk analysis was performed with the Newcastle-Ottawa Scale. Results: Five studies were included, using different analyzes, whose results varied. Odds ratio ranged from 1.21 to 2.59 (95% CI = 1.01 - 3.28); relative risk was 1.68 (95% CI = 1.50 - 1.87), sensitivity from 3 to 24%, specificity from 90 to 99%, PPV from 42 to 63%, NPV from 72 to 75% and AUC from 51 to 58%. Conclusion: The DS can be a test whose presence of movement dysfunctions is a predictor of the risk of musculoskeletal injuries in individuals who practice physical exercises. However, due to the methodological limitations presented, caution is suggested when interpreting such results. PROSPERO registration: CRD4201706922.


2013 ◽  
Vol 45 (11) ◽  
pp. 2144-2150 ◽  
Author(s):  
REBECCA E. FRIMENKO ◽  
W. BRENT LIEVERS ◽  
PATRICK O. RILEY ◽  
JOSEPH S. PARK ◽  
MACALUS V. HOGAN ◽  
...  

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