Investigation of an Instrumented Mouthguard for Measuring Head Impact Exposure

用于测量头部碰撞暴露的仪器化护齿套的研究

基本信息

  • 批准号:
    8491136
  • 负责人:
  • 金额:
    $ 23.55万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2013
  • 资助国家:
    美国
  • 起止时间:
    2013-04-01 至 2015-03-31
  • 项目状态:
    已结题

项目摘要

DESCRIPTION (provided by applicant): Concussion is a widespread problem in the military and in sports, with over 1 million estimated sports related concussions occurring in the U.S. each year. However, the mechanism of injury, and therefore diagnosis, is not well understood. Diagnosis of concussion is notoriously difficult because the symptoms, and their interpretation by physicians, are highly variable and subjective. An even greater challenge is that rarely do youth sporting events have trained medical personnel to identify and evaluate injured athletes. Proper management of injury is essential to avoid short-term risks such as re- injury, or even brain hemorrhage, as well as long-term risks ranging from cognitive deficits to dementia. Therefore, an unbiased and quantitative measure of concussion in real-time is needed to prevent further injury. This proposed project centers on the use of a novel instrumented mouthguard that will be worn by collegiate football players in practice and in games to measure three axes of both linear and rotational acceleration. Recent research emphasizes the potential importance of rotational acceleration in concussions as well as the importance of cumulative impacts in long-term neurodegenerative diseases. We hypothesize that this mouthguard can accurately detect magnitudes of linear and rotational acceleration of the head as well as record head impact events that occur during play. The goal of this R21 proposal is to appropriately confirm this hypothesis both in laboratory and on the field. This will enable larger studies to be performed that may better elucidate the mechanism of concussive injury and the risk of neurodegenerative disease. We will do so through the following specific aims: (1) Validate mouthguard kinematics in the laboratory using an instrumented dummy head and (2) Verify impact detection in vivo using practice and game video. In order to validate the mouthguard, head acceleration measurements from mouthguard will be compared to data captured by the instrumented dummy head while undergoing a standard head impact test. In addition, high-speed speed video captured in practice will be used to reconstruct real impacts in 3D to assess validity of laboratory head impact system. Finally, head impact events from mouthguards will be confirmed using game video to modify post-process algorithms in order to optimize the accuracy in detecting real head impact events. Achieving these 2 aims will be the basis for future multi-site clinical studies on the mechanism of traumatic brain injury in multiple sports.
描述(由申请人提供):脑震荡是军队和体育运动中普遍存在的问题,估计美国每年发生超过100万起与运动有关的脑震荡。然而,损伤的机制,因此诊断,没有很好地理解。脑震荡的诊断是出了名的困难,因为症状和医生对症状的解释是高度可变和主观的。一个更大的挑战是,青年体育赛事很少培训医务人员来识别和评估受伤的运动员。适当的损伤管理对于避免再损伤甚至脑出血等短期风险以及从认知缺陷到痴呆的长期风险至关重要。因此,需要对脑震荡进行实时的公正和定量测量,以防止进一步的伤害。该项目的核心是使用一种新型的仪器化护齿器,这种护齿器将由大学足球运动员在练习和比赛中佩戴,以测量线性和旋转加速度的三个轴。最近的研究强调了旋转加速度在脑震荡中的潜在重要性以及累积影响在长期神经退行性疾病中的重要性。我们假设,这种护齿套可以准确地检测头部的线性和旋转加速度的大小,以及记录在播放过程中发生的头部撞击事件。 R21提案的目的是在实验室和现场适当地证实这一假设。这将使更大规模的研究得以进行,从而更好地阐明脑震荡损伤的机制和神经退行性疾病的风险。我们将通过以下具体目标来实现:(1)在实验室中使用装有仪器的假人头部来验证护齿器的运动学;(2)使用练习和比赛视频来验证体内的撞击检测。为了验证护齿套,将护齿套的头部加速度测量值与在进行标准头部撞击测试时由仪器化假人头部捕获的数据进行比较。此外,在实践中捕获的高速视频将被用来重建三维真实的影响,以评估实验室头部碰撞系统的有效性。最后,将使用游戏视频来确认来自护齿器的头部撞击事件,以修改后处理算法,从而优化检测真实的头部撞击事件的准确性。实现这两个目标将是未来多地点临床研究的基础上创伤性脑损伤的机制,在多种运动。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

David Camarillo其他文献

David Camarillo的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('David Camarillo', 18)}}的其他基金

A youth-specific helmet for preventing traumatic brain injury
预防创伤性脑损伤的青少年专用头盔
  • 批准号:
    10268212
  • 财政年份:
    2020
  • 资助金额:
    $ 23.55万
  • 项目类别:
A youth-specific helmet for preventing traumatic brain injury
预防创伤性脑损伤的青少年专用头盔
  • 批准号:
    10082152
  • 财政年份:
    2020
  • 资助金额:
    $ 23.55万
  • 项目类别:
Investigation of an Instrumented Mouthguard for Measuring Head Impact Exposure
用于测量头部碰撞暴露的仪器化护齿套的研究
  • 批准号:
    8670054
  • 财政年份:
    2013
  • 资助金额:
    $ 23.55万
  • 项目类别:
Investigation of an Instrumented Mouthguard for Measuring Head Impact Exposure
用于测量头部碰撞暴露的仪器化护齿套的研究
  • 批准号:
    8635350
  • 财政年份:
    2013
  • 资助金额:
    $ 23.55万
  • 项目类别:

相似海外基金

DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
  • 批准号:
    EP/Y029089/1
  • 财政年份:
    2024
  • 资助金额:
    $ 23.55万
  • 项目类别:
    Research Grant
CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
  • 批准号:
    2337776
  • 财政年份:
    2024
  • 资助金额:
    $ 23.55万
  • 项目类别:
    Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
  • 批准号:
    2338816
  • 财政年份:
    2024
  • 资助金额:
    $ 23.55万
  • 项目类别:
    Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
  • 批准号:
    2338846
  • 财政年份:
    2024
  • 资助金额:
    $ 23.55万
  • 项目类别:
    Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
  • 批准号:
    2348261
  • 财政年份:
    2024
  • 资助金额:
    $ 23.55万
  • 项目类别:
    Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
  • 批准号:
    2348346
  • 财政年份:
    2024
  • 资助金额:
    $ 23.55万
  • 项目类别:
    Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
  • 批准号:
    2348457
  • 财政年份:
    2024
  • 资助金额:
    $ 23.55万
  • 项目类别:
    Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
  • 批准号:
    2404989
  • 财政年份:
    2024
  • 资助金额:
    $ 23.55万
  • 项目类别:
    Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
  • 批准号:
    2339310
  • 财政年份:
    2024
  • 资助金额:
    $ 23.55万
  • 项目类别:
    Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
  • 批准号:
    2339669
  • 财政年份:
    2024
  • 资助金额:
    $ 23.55万
  • 项目类别:
    Continuing Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了