权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

SCH：INT：通过影响补偿降低创伤性脑损伤风险

基本信息

批准号：
1622741
负责人：
Mark Minor
金额：
$ 174.74万
依托单位：
University of Utah
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1622741&HistoricalAwards=false
关键词：
SCH INT Reducing Traumatic Brain

项目摘要

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.

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控制这种疾病很重要，但预防脑外伤的发生也是解决方案的关键组成部分。该提案的目标是通过智能技术收集感官数据来实时预测和表征头部撞击，优化基于这些撞击特征的保护机制，并将撞击创伤属性发送到临床数据库以进行进一步分析和损伤风险预测，从而降低创伤性脑损伤的风险。这项技术将大大改善机动车碰撞、运动和工业事故中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)实时态势监测，以预测何时以及如何发生危险的撞击；(2)主动预防机制，以降低脑损伤撞击的风险。该技术的初步评估是在运动环境中进行的，但系统组件可以广泛适应机动车辆，工业安全帽和老年人生活环境的实施。本提案的研究目标是：(1)通过先进的情境监测、肌肉骨骼激活和减少撞击特定力来降低创伤性脑损伤的风险；(2)提高基于多尺度脑变形模型的颅脑损伤风险潜在识别。这些目标是通过整合四个基础研究工作来实现的。首先，基于射频（RF）传感、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、陀螺仪和磁力计结合使用时，这些算法提供检测物体所需的传感能力，捕获周围物体的方向速度数据，并实时处理数据以确定即将发生碰撞的概率和特征。其次，研究了听觉警告后的肌肉骨骼握紧，作为最小化头部或身体撞击后的头部角加速度的一种手段。听觉警告线索和肌肉握紧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响应时间以及肌肉激活参数，以最好地减轻碰撞期间头部角加速度。第三，针对即将到来的冲击特性，采用独特的可控充气气囊实现主动力诱导。膀胱的最佳压力和挠度特性基于冲击速度和方向，并通过一种新的三维多尺度人头有限元模型进行评估。该模型结合了脑-颅骨界面微观结构的解剖变异，这是预测头部损伤的关键区域。第四个基础研究领域采用多尺度模型研究头部冲击力和加速度与撞击时脑组织局部变形的关系。这些研究将用于改善从冲击运动学对TBI风险的预测。