SCH: INT: Reducing Traumatic Brain Injury Risk with Impact Compensation

University Of Utah, Salt Lake City UT

Investigators

Abstract

Traumatic brain injury is a leading cause of death and disability in the United States. Over 1.7 million people sustain a brain injury each year and make up one-third of all injuries seen in the emergency room. Developing rehabilitation and treatment strategies to manage this disease are important, but preventing the occurrence of brain trauma is also critical component to the solution. The goal of this proposal is to reduce the risk of traumatic brain injury through smart technology that collects sensory data to predict and characterize head impact in real-time, optimizes protective mechanisms based on those impact characteristics, and sends impact trauma attributes to a clinical database for further analysis and injury risk prediction. This technology will substantially improve traumatic brain injury prevention and diagnosis in motor vehicle crashes, sports, and industrial accidents. To accomplish this goal, fundamental research efforts include (1) real-time situational monitoring to predict when and how dangerous impacts are about to occur and (2) active prevention mechanisms to reduce the risk of brain injury impact. Initial evaluation of the technology is in a sports setting, but the system components can be widely adaptive for implementation in motor vehicles, industrial safety helmets, and living environments for the elderly. The research goals of this proposal are to (1) reduce the risk of traumatic brain injury through advanced situational monitoring, musculoskeletal activation, and impact-specific force reduction; and (2) to improve potential identification of head injury risk based on multiscale brain deformation modeling. These goals are accomplished by integrating four fundamental research efforts. First, tracking and collision detection algorithms are developed based on radio frequency (RF) sensing, processing, and flexible antenna design. When used in conjunction with triaxial accelerometers, gyroscopes, and magnetometers, these algorithms provide the sensing capabilities required to detect objects, capture directional velocity data of surrounding objects, and process data in real-time to determine probabilities and characteristics of impending collision. Second, musculoskeletal clenching following auditory warning is investigated as a means of minimizing head angular acceleration following head or body impact. The development of auditory warning cues and muscle clench strategies utilizes kinematic musculoskeletal modeling and human subject studies to identify required auditory cues and response times as well as muscle activation parameters that best mitigate head angular acceleration during a collision. Third, active force reduction specific to impending impact characteristics are implemented using a unique controllable air-filled bladder. Optimal pressure and deflection characteristics of the bladder are based on impact velocity and direction, and evaluated with a novel three-dimensional multiscale finite element model of the human head. This model incorporates anatomical variability in the microstructures at the brain-skull interface, a region that is critical to predictions of head injury. The fourth fundamental research area uses the multiscale model to investigate the relationship of head impact force and acceleration to regional deformation of brain tissue upon impact. These studies will be used to improve predictions of TBI risk from impact kinematics.