Methodological Aspects of an Experimental Research on Cerebral Tolerance on the Basis of Boxers' Training Fights

Chamouard, F; Trosseille, Xavier; Pincemaille, Y.; Tarrière, C

doi:10.4271/872195

Cited by 3 publications

(2 citation statements)

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“…Researchers also mounted triaxial accelerometers on custom-made headgear fitted to volunteer boxers in order to obtain human head kinematics and physiological effects. They found angular accelerations higher than 3500 rad/s 2 , exceeding proposed tolerance thresholds for volunteers; a maximum angular velocity of 48 rad/s and linear accelerations of 159 g were also reported (Chamouard et al, 1987;Pincemaille et al, 1989).…”

Section: Other Wearable Sensorsmentioning

confidence: 84%

“…A more recent approach has been to collect data from sensors worn by contact sport athletes so that clinical injury outcomes can be related to engineering parameters to develop injury risk functions. Researchers have used various types of sensor-mounted headgear (Chamouard et al, 1987;Duma et al, 2005). instrumented mouth guards (Camarillo et al, 2013;D.…”

Section: Injury Risk Functionsmentioning

confidence: 99%

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Evaluation of the Efficacy of Head and Brain Injury Risk Functions

Sanchez¹

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Head and brain injury risk functions have been proposed over the years in order to estimate the probability of brain injury from head impact using kinematic and injury data from various sources, such as PMHS and animal testing. Yet, researchers have reiterated the need for human head kinematic data for creating and evaluating injury risk functions and risk assessment values given that other types of data may lack the physiologic and anthropometric response required. To this end, this thesis aimed to collect human head kinematics to evaluate existing injury risk functions. A literature review revealed what measures of human head motion have been previously published, including direct measures of head kinematics (e.g., wearable sensors used on-field with contact athletes) and indirect measures of head motion (e.g., computational and experimental reconstructions of real-life impacts). Three data sources (n = 443) were selected from the literature based on inclusion criteria. Data were evaluated for consistency and used to calculate kinematic and strain-based injury risks. The injury risk values were used to evaluate the efficacy of each of 16 injury risk functions using four separate analytical tools. Correlations between the injury risk functions and strain metrics showed that strain-and rotationally-based injury risk functions had the strongest correlations. Area under ROC curves assessed each function's ability to separate injurious and non-injurious impacts; all risk functions were better than random guessing. Likelihood estimates ranked the injury risk functions on their ability to correctly predict the dataset's injury outcomes, with GAMBIT showing the best predictive capability according to this measure. The number of expected injuries was calculated for each risk function; however, most did not correctly estimate the number of observed injuries (n = 31). Results from these four assessment tools, however, were mixed, with no single risk function performing best. The lack of consensus in the assessment tools may be a result of the data used to develop the risk functions. Studies have noted unbiased exposure data is needed to estimate the absolute injury risk from impact; however, most injury risk functions (and the data in this thesis) were based on case-control data. Statistical simulations were conducted to create injury risk functions based on several sampling scenarios. These results from these simulations demonstrated that researchers should be wary of risk curves derived solely from case-controlled data given that these may be over-predictive of injury probabilities compared to the absolute risk of a population. Despite the issues with the risk functions, this thesis provides a verified data set that is a necessary step for injury risk function evaluation.

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Section: Other Wearable Sensorsmentioning

confidence: 84%

Section: Injury Risk Functionsmentioning

confidence: 99%

Evaluation of the Efficacy of Head and Brain Injury Risk Functions

Sanchez¹

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Evaluation of Head and Brain Injury Risk Functions Using Sub-Injurious Human Volunteer Data

Sanchez

Gabler

McGhee

et al. 2017

Journal of Neurotrauma

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Risk assessment models are developed to estimate the probability of brain injury during head impact using mechanical response variables such as head kinematics and brain tissue deformation. Existing injury risk functions have been developed using different datasets based on human volunteer and scaled animal injury responses to impact. However, many of these functions have not been independently evaluated with respect to laboratory-controlled human response data. In this study, the specificity of 14 existing brain injury risk functions was assessed by evaluating their ability to correctly predict non-injurious response using previously conducted sled tests with well-instrumented human research volunteers. Six degrees-of-freedom head kinematics data were obtained for 335 sled tests involving subjects in frontal, lateral, and oblique sled conditions up to 16 Gs peak sled acceleration. A review of the medical reports associated with each individual test indicated no clinical diagnosis of mild or moderate brain injury in any of the cases evaluated. Kinematic-based head and brain injury risk probabilities were calculated directly from the kinematic data, while strain-based risks were determined through finite element model simulation of the 335 tests. Several injury risk functions substantially over predict the likelihood of concussion and diffuse axonal injury; proposed maximum principal strain-based injury risk functions predicted nearly 80 concussions and 14 cases of severe diffuse axonal injury out of the 335 non-injurious cases. This work is an important first step in assessing the efficacy of existing brain risk functions and highlights the need for more predictive injury assessment models.

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