Smart Mouthguard: Bio-analytics for the development of a respiratory sensing device

智能护齿套：用于开发呼吸传感设备的生物分析

• 批准号: BB/N013352/1
• 负责人: Hua Ye
• 金额: $ 18.99万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FN013352%2F1
• 关键词: Smart; Mouthguard; Bio; analytics; development

Injuries during sports participation are often caused by systemic failure due to physical exertion. Breathing parameters can be used to measure changes in perceived exertion allowing the athlete to monitor and check how they train or play the game. A smart mouthguard is being developed that incorporates a novel breathing technology to assess and track performance. It measures breathing, which can be analysed using model based analytics to estimate physical fatigue. The metrics will aid contact sport athletes in understanding their performance and assist in personalising their training regime without the requirement to wear any additional gear. It can also be used to make decisions regarding the potential injury risk athletes face during training or competition. This is particular relevant as more sport federations are making mouthguard use compulsory for all participants, while at the same time aiming to reduce overall injurie rates due to contact sport participation.As athletic participation continues to increase around the world, the need for mouthguards will continue to climb. It has been estimated that over 40 million mouthguards are sold annually in the US alone. This number is expected to grow proportional with the increase of contact sport athletes. There is a strong need within the contact sports community to show that these sports can be played safely, as contact sports include some of the fastest growing sports in the world.Accurate breathing analysis is required in order to provide good estimates of physical fatigue. The breathing signal is noisy and can vary widely between people. A computational model of the breathing signal reduces this noise and allows for better estimates of physical exertion. The University of Oxford with its expertise in bio-analytics will work together with Chiaro Ltd to develop a robust approach for tracking fatigue using a smart mouthguard. The project will consist of sharing knowledge on the best analytical modelling techniques and hardware designs to develop a user friendly smart mouthguard. The developed analytical model is tested using lab-based data and model modifications are made before applying it to field testing. The project shows how certain analytical techniques developed within other BBSRC's funded projects can successfully be translated to other industries.

参加体育运动时受伤往往是由于体力消耗导致全身衰竭造成的。呼吸参数可用于测量感知用力的变化，使运动员能够监控和检查他们的训练或比赛方式。正在开发一种智能护齿套，该护齿套采用了新颖的呼吸技术来评估和跟踪表现。它测量呼吸，可以使用基于模型的分析来分析呼吸以估计身体疲劳。这些指标将帮助接触运动运动员了解他们的表现，并帮助个性化他们的训练制度，而无需穿戴任何额外的装备。它还可用于就运动员在训练或比赛期间面临的潜在伤害风险做出决策。这一点尤其重要，因为越来越多的体育联合会强制要求所有参与者使用护齿套，同时旨在降低因参与接触性运动而导致的总体受伤率。随着世界各地运动参与的不断增加，对护齿套的需求将继续增加攀爬。据估计，仅在美国每年售出超过 4000 万个护齿套。预计这个数字将随着接触运动运动员的增加而成比例增长。接触运动界迫切需要证明这些运动可以安全进行，因为接触运动包括一些世界上发展最快的运动。需要准确的呼吸分析，以便提供对身体疲劳的良好估计。呼吸信号很嘈杂，并且人与人之间的差异很大。呼吸信号的计算模型可以减少这种噪音，并可以更好地估计体力消耗。拥有生物分析专业知识的牛津大学将与 Chiaro Ltd 合作，开发一种使用智能护齿套追踪疲劳的可靠方法。该项目将包括分享最佳分析建模技术和硬件设计的知识，以开发用户友好的智能护齿套。使用基于实验室的数据对开发的分析模型进行测试，并在将其应用于现场测试之前对模型进行修改。该项目展示了如何将其他 BBSRC 资助项目中开发的某些分析技术成功地转化为其他行业。

项目成果

Probabilistic Sensor Network Design

• DOI: 10.1109/bsn.2016.7516234
• 发表时间: 2016-01-01
• 期刊: 2016 IEEE 13TH INTERNATIONAL CONFERENCE ON WEARABLE AND IMPLANTABLE BODY SENSOR NETWORKS (BSN)
• 作者: Bergmann, Jeroen;Noble, Alison;Thompson, Mark
• 通讯作者: Thompson, Mark

Determining the performance of a temperature sensor embedded into a mouthguard.

• DOI: 10.1038/s41405-022-00114-8
• 发表时间: 2022-08-01
• 期刊: BDJ OPEN
• 影响因子: 3
• 作者: de Almeida E Bueno, Leonardo;Milnthorpe, William;Bergmann, Jeroen H M
• 通讯作者: Bergmann, Jeroen H M

HeadSLAM: Pedestrian SLAM with Head-Mounted Sensors.

• DOI: 10.3390/s22041593
• 发表时间: 2022-02-18
• 期刊: Sensors (Basel, Switzerland)
• 作者: Hou X;Bergmann J
• 通讯作者: Bergmann J

Applying ubiquitous sensing to estimate perceived exertion based on cardiorespiratory features

• DOI: 10.1007/s12283-021-00346-1
• 发表时间: 2021-04-22
• 期刊: SPORTS ENGINEERING
• 影响因子: 1.7
• 作者: Bueno, Leonardo;Kwong, Man Ting;Bergmann, Jeroen H. M.
• 通讯作者: Bergmann, Jeroen H. M.

A Novel Method for the Mechanical Testing of Sports Mouthguards

运动护齿套机械测试的新方法

• DOI: 10.3390/app12073449
• 发表时间: 2022
• 期刊: Applied Sciences
• 作者: Goldberg T