Rotational Head Kinematics in Football Impacts: An Injury Risk Function for Concussion

The increased incidence of injury demonstrated in epidemiological data for the elderly population, and females compared to males, has not been fully understood in the context of the biomechanical response to impact. A contributing factor to these differences in injury risk could be the variation in geometry between young and aged persons and between males and females. In this study, a new methodology, coupling a CAD and a repositioning software, was developed to reposture an existing Finite element neck while retaining a high level of mesh quality. A 5th percentile female aged neck model (F0575YO) and a 50th percentile male aged neck model (M5075YO) were developed from existing young (F0526YO and M5026YO) neck models (Global Human Body Models Consortium v5.1). The aged neck models included an increased cervical lordosis and an increase in the facet joint angles, as reported in the literature. The young and the aged models were simulated in frontal (2, 8, and 15 g) and rear (3, 7, and 10 g) impacts. The responses were compared using head and relative facet joint kinematics, and nominal intervertebral disc shear strain. In general, the aged models predicted higher tissue deformations, although the head kinematics were similar for all models. In the frontal impact, only the M5075YO model predicted hard tissue failure, attributed to the combined effect of the more anteriorly located head with age, when compared to the M5026YO, and greater neck length relative to the female models. In the rear impacts, the F0575YO model predicted higher relative facet joint shear compared to the F0526YO, and higher relative facet joint rotation and nominal intervertebral disc strain compared to the M5075YO. When comparing the male models, the relative facet joint kinematics predicted by the M5026YO and M5075YO were similar. The contrast in response between the male and female models in the rear impacts was attributed to the higher lordosis and facet angle in females compared to males. Epidemiological data reported that females were more likely to sustain Whiplash Associated Disorders in rear impacts compared to males, and that injury risk increases with age, in agreement with the findings in the present study. This study demonstrated that, although the increased lordosis and facet angle did not affect the head kinematics, changes at the tissue level were considerable (e.g., 26% higher relative facet shear in the female neck compared to the male, for rear impact) and relatable to the epidemiology. Future work will investigate tissue damage and failure through the incorporation of aged material properties and muscle activation.

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Vehicle deformation depth based injury risk function for safety benefit evaluation of crash avoidance and mitigation systems

IET Intelligent Transport Systems ◽

10.1049/iet-its.2017.0150 ◽

2018 ◽

Vol 12 (5) ◽

pp. 386-393 ◽

Cited By ~ 2

Author(s):

Long Chen ◽

Yugong Luo ◽

Fabrizio Stefano Napolitano ◽

Robert Zobel ◽

Keqiang Li

Keyword(s):

Injury Risk ◽

Risk Function ◽

Benefit Evaluation ◽

Crash Avoidance ◽

Safety Benefit

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

Journal of Biomechanical Engineering ◽

10.1115/1.4042012 ◽

2018 ◽

Vol 141 (2) ◽

Cited By ~ 1

Author(s):

Ann M. Bailey ◽

Timothy L. McMurry ◽

Robert S. Salzar ◽

Jeff R. Crandall

Keyword(s):

Injury Risk ◽

Risk Function ◽

Axial Loading ◽

Survival Model ◽

Natural Period ◽

Axial Impact ◽

Injury Prediction ◽

Leg Injuries ◽

Risk Functions ◽

The Military

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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Influence of Strain Post-Processing on Brain Injury Prediction

10.1101/2021.07.15.452485 ◽

2021 ◽

Author(s):

Madelen Fahlstedt ◽

Shiyang Meng ◽

Svein Kleiven

Keyword(s):

Brain Injury ◽

Linear Correlation ◽

Injury Risk ◽

Risk Function ◽

American Football ◽

Head Model ◽

Post Processing ◽

Injury Prediction ◽

Risk Functions ◽

Institute Of Technology

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.

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Deep Learning Model for Predicting Head Kinematics from Crash Videos

10.21203/rs.3.rs-1083278/v1 ◽

2021 ◽

Author(s):

Vikas Hasija ◽

Erik G. Takhounts

Keyword(s):

Neural Network ◽

Deep Learning ◽

Injury Risk ◽

Time History ◽

Learning Model ◽

Wearable Devices ◽

Body Region ◽

Head Kinematics ◽

Angular Velocities ◽

Deep Learning Model

Abstract Head kinematics information is very valuable as it is used to measure brain injury risk. Currently, head kinematics are measured using wearable devices or instrumentation mounted on the head. These instrumentation and wearable devices can have errors due to faulty sensors and due to relative motion between the wearable device and the respective body region. This paper proposes a novel method to predict the head kinematics directly from videos without any instrumentation using a deep learning approach. To prove the concept, a deep learning model was developed for predicting time history of head angular velocities and their respective peaks using Finite Element (FE) based crash simulation data. This FE dataset was split into training, validation, and test datasets. A combined Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) based deep learning model was developed using the training and validations sets. The test (unseen) dataset was used to evaluate the predictive capability of the deep learning model. On the test dataset, correlation coefficient obtained between the actual and predicted peak angular velocities was 0.73, 0.85, and 0.92 for X, Y, and Z components respectively.

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Injury risk function for the small female wrist in axial loading

Accident Analysis & Prevention ◽

10.1016/s0001-4575(02)00092-1 ◽

2003 ◽

Vol 35 (6) ◽

pp. 869-875 ◽

Cited By ~ 16

Author(s):

Stefan M. Duma ◽

Brian M. Boggess ◽

Jeff R. Crandall ◽

Conor B. MacMahon

Keyword(s):

Injury Risk ◽

Risk Function ◽

Axial Loading ◽

Small Female

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Clinical Update: Functional Capacity Evaluations—An Update

Guides Newsletter ◽

10.1001/amaguidesnewsletters.1999.sepoct02 ◽

1999 ◽

Vol 4 (5) ◽

pp. 4-7 ◽

Cited By ~ 1

Author(s):

Laura Welch

Keyword(s):

Job Performance ◽

Functional Capacity ◽

Injury Risk ◽

Disability Evaluation ◽

Time Period ◽

Submaximal Effort ◽

Lack Of Sleep ◽

Overall Performance ◽

Time Required ◽

Specific Limitation

Abstract Functional capacity evaluations (FCEs) have become an important component of disability evaluation during the past 10 years to assess an individual's ability to perform the essential or specific functions of a job, both preplacement and during rehabilitation. Evaluating both job performance and physical ability is a complex assessment, and some practitioners are not yet certain that an FCE can achieve these goals. An FCE is useful only if it predicts job performance, and factors that should be assessed include overall performance; consistency of performance across similar areas of the FCE; consistency between observed behaviors during the FCE and limitations or abilities reported by the worker; objective changes (eg, blood pressure and pulse) that are appropriate relative to performance; external factors (illness, lack of sleep, or medication); and a coefficient of variation that can be measured and assessed. FCEs can identify specific movement patterns or weaknesses; measure improvement during rehabilitation; identify a specific limitation that is amenable to accommodation; and identify a worker who appears to be providing a submaximal effort. FCEs are less reliable at predicting injury risk; they cannot tell us much about endurance over a time period longer than the time required for the FCE; and the FCE may measure simple muscular functions when the job requires more complex ones.

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