Consensus Head Acceleration Measurement Practices (CHAMP): Laboratory Validation of Wearable Head Kinematic Devices.

Wearable devices are increasingly used to measure real-world head impacts and study brain injury mechanisms. These devices must undergo validation testing to ensure they provide reliable and accurate information for head impact sensing, and controlled laboratory testing should be the first step of validation. Past validation studies have applied varying methodologies, and some devices have been deployed for on-field use without validation.

Integrating Human and Nonhuman Primate Data to Estimate Human Tolerances for Traumatic Brain Injury.

Traumatic brain injury (TBI) contributes to a significant portion of the injuries resulting from motor vehicle crashes, falls, and sports collisions. The development of advanced countermeasures to mitigate these injuries requires a complete understanding of the tolerance of the human brain to injury. In this study, we developed a new method to establish human injury tolerance levels using an integrated database of reconstructed football impacts, subinjurious human volunteer data, and nonhuman primate data.

Development of a Low-Power Instrumented Mouthpiece for Directly Measuring Head Acceleration in American Football.

Instrumented mouthpieces (IM) offer a means of measuring head impacts that occur in sport. Direct measurement of angular head kinematics is preferential for accuracy; however, existing IMs measure angular velocity and differentiate the measurement to calculate angular acceleration, which can limit bandwidth and consume more power. This study presents the development and validation of an IM that uses new, low-power accelerometers for direct measurement of linear and angular acceleration over a broad range of head impact conditions in American football.

On-Field Performance of an Instrumented Mouthguard for Detecting Head Impacts in American Football.

Wearable sensors that accurately record head impacts experienced by athletes during play can enable a wide range of potential applications including equipment improvements, player education, and rule changes. One challenge for wearable systems is their ability to discriminate head impacts from recorded spurious signals. This study describes the development and evaluation of a head impact detection system consisting of a mouthguard sensor and machine learning model for distinguishing head impacts from spurious events in football games.

Development and Evaluation of a Test Method for Assessing the Performance of American Football Helmets.

As more is learned about injury mechanisms of concussion and scenarios under which injuries are sustained in football games, methods used to evaluate protective equipment must adapt. A combination of video review, videogrammetry, and laboratory reconstructions was used to characterize concussive impacts from National Football League games during the 2015-2017 seasons. Test conditions were generated based upon impact locations and speeds from this data set, and a method for scoring overall helmet performance was created.

A Multibody Model for Predicting Spatial Distribution of Human Brain Deformation Following Impact Loading.

With an increasing focus on long-term consequences of concussive brain injuries, there is a new emphasis on developing tools that can accurately predict the mechanical response of the brain to impact loading. Although finite element models (FEM) estimate the brain response under dynamic loading, these models are not capable of delivering rapid (∼seconds) estimates of the brain's mechanical response. In this study, we develop a multibody spring-mass-damper model that estimates the regional motion of the brain to rotational accelerations delivered either about one anatomic axis or across three orthogonal axes simultaneously.

Development of a Second-Order System for Rapid Estimation of Maximum Brain Strain.

Diffuse brain injuries are assessed with deformation-based criteria that utilize metrics based on rotational head kinematics to estimate brain injury severity. Although numerous metrics have been proposed, many are based on empirically-derived models that use peak kinematics, which often limit their applicability to a narrow range of head impact conditions. However, over a broad range of impact conditions, brain deformation response to rotational head motion behaves similarly to a second-order mechanical system, which utilizes the full kinematic time history of a head impact.

Brain tissue strains vary with head impact location: A possible explanation for increased concussion risk in struck versus striking football players.

Background: On-field football helmet impacts over a large range of severities have caused concussions in some players but not in other players. One possible explanation for this variability is the struck player's helmet impact location.

Methods: We examined the effect of impact location on regional brain tissue strain when input energy was held constant.

Development of a Metric for Predicting Brain Strain Responses Using Head Kinematics.

Diffuse brain injuries are caused by excessive brain deformation generated primarily by rapid rotational head motion. Metrics that describe the severity of brain injury based on head motion often do not represent the governing physics of brain deformation, rendering them ineffective over a broad range of head impact conditions. This study develops a brain injury metric based on the response of a second-order mechanical system, and relates rotational head kinematics to strain-based brain injury metrics: maximum principal strain (MPS) and cumulative strain damage measure (CSDM).

A reanalysis of football impact reconstructions for head kinematics and finite element modeling.

Background: Head kinematics generated by laboratory reconstructions of professional football helmet impacts have been applied to computational models to study the biomechanics of concussion. Since the original publication of this data, techniques for evaluating accelerometer consistency and error correction have been developed. This study applies these techniques to the original reconstruction data and reanalyzes the results given the current state of concussion biomechanics.

Development of a Single-Degree-of-Freedom Mechanical Model for Predicting Strain-Based Brain Injury Responses.

Linking head kinematics to injury risk has been the focus of numerous brain injury criteria. Although many early forms were developed using mechanics principles, recent criteria have been developed using empirical methods based on subsets of head impact data. In this study, a single-degree-of-freedom (sDOF) mechanical analog was developed to parametrically investigate the link between rotational head kinematics and brain deformation.

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

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.

Assessment of Kinematic Brain Injury Metrics for Predicting Strain Responses in Diverse Automotive Impact Conditions.

Numerous injury criteria have been developed to predict brain injury using the kinematic response of the head during impact. Each criterion utilizes a metric that is some mathematical combination of the velocity and/or acceleration components of translational and/or rotational head motion. Early metrics were based on linear acceleration of the head, but recent injury criteria have shifted towards rotational-based metrics.