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

在美国，创伤性脑损伤是导致死亡和残疾的主要原因。每年有超过170万人遭受脑损伤，占急诊室所有损伤的三分之一。制定康复和治疗策略来管理这种疾病是重要的，但预防脑损伤的发生也是解决方案的关键组成部分。这项建议的目标是通过智能技术来降低创伤性脑损伤的风险，该技术收集感官数据来实时预测和表征头部撞击，根据这些撞击特征优化保护机制，并将撞击创伤属性发送到临床数据库进行进一步分析和损伤风险预测。这项技术将大大提高在机动车撞车、运动和工业事故中对创伤性脑损伤的预防和诊断。为了实现这一目标，基础研究工作包括(1)实时情况监测，以预测危险影响何时发生以及如何发生，以及(2)主动预防机制，以降低脑损伤影响的风险。该技术的初步评估是在运动环境中，但系统组件可以广泛适用于机动车辆、工业安全头盔和老年人的生活环境。本提案的研究目标是：(1)通过先进的情境监测、肌肉骨骼激活和特定冲击力的减少，降低创伤性脑损伤的风险；(2)基于多尺度脑形成模型，提高对头部损伤风险的潜在识别。这些目标是通过整合四项基础研究工作来实现的。首先，基于射频(RF)感知、处理和柔性天线设计，开发了跟踪和碰撞检测算法。当与三轴加速度计、陀螺仪和磁力计一起使用时，这些算法提供了检测物体、捕获周围物体的方向速度数据和实时处理数据以确定即将发生碰撞的概率和特征所需的传感能力。其次，研究了听觉警告后的肌肉骨骼紧握，作为一种最小化头部或身体撞击后头部角加速度的方法。听觉警告线索和肌肉紧握策略的发展利用运动学肌肉骨骼建模和人体受试者研究来确定所需的听觉线索和反应时间，以及最好地减轻碰撞期间头部角加速度的肌肉激活参数。第三，使用独特的可控充气气囊实现了针对即将到来的冲击特性的主动强制教育。膀胱的最佳压力和偏转特性基于撞击速度和方向，并用一种新的三维多尺度人体头部有限元模型进行评估。该模型结合了脑-颅界面微结构的解剖变异，该区域是预测头部损伤的关键区域。第四个基础研究区域使用多尺度模型来研究头部撞击力和加速度与撞击时脑组织局部变形的关系。这些研究将用于改进冲击运动学对脑外伤风险的预测。

Traumatic brain injury is a leading cause of death and disability in the United States. Over 1.7million people sustain a brain injury each year and make up one-third of all injuries seen in theemergency room. Developing rehabilitation and treatment strategies to manage this disease areimportant, 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 technologythat collects sensory data to predict and characterize head impact in real-time, optimizes protectivemechanisms based on those impact characteristics, and sends impact trauma attributes to a clinicaldatabase for further analysis and injury risk prediction. This technology will substantially improvetraumatic brain injury prevention and diagnosis in motor vehicle crashes, sports, and industrialaccidents. To accomplish this goal, fundamental research efforts include (1) real-time situationalmonitoring to predict when and how dangerous impacts are about to occur and (2) activeprevention mechanisms to reduce the risk of brain injury impact. Initial evaluation of thetechnology is in a sports setting, but the system components can be widely adaptive forimplementation in motor vehicles, industrial safety helmets, and living environments for theelderly.The research goals of this proposal are to (1) reduce the risk of traumatic brain injury throughadvanced situational monitoring, musculoskeletal activation, and impact-specific force reduction;and (2) to improve potential identification of head injury risk based on multiscale braindeformation modeling. These goals are accomplished by integrating four fundamental researchefforts. First, tracking and collision detection algorithms are developed based on radio frequency(RF) sensing, processing, and flexible antenna design. When used in conjunction with triaxialaccelerometers, gyroscopes, and magnetometers, these algorithms provide the sensing capabilitiesrequired to detect objects, capture directional velocity data of surrounding objects, and processdata in real-time to determine probabilities and characteristics of impending collision. Second,musculoskeletal clenching following auditory warning is investigated as a means of minimizinghead angular acceleration following head or body impact. The development of auditory warningcues and muscle clench strategies utilizes kinematic musculoskeletal modeling and human subjectstudies to identify required auditory cues and response times as well as muscle activationparameters that best mitigate head angular acceleration during a collision. Third, active forcereduction specific to impending impact characteristics are implemented using a unique controllableair-filled bladder. Optimal pressure and deflection characteristics of the bladder are based onimpact velocity and direction, and evaluated with a novel three-dimensional multiscale finiteelement model of the human head. This model incorporates anatomical variability in themicrostructures 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 ofhead impact force and acceleration to regional deformation of brain tissue upon impact. Thesestudies will be used to improve predictions of TBI risk from impact kinematics.