利用增强运动录像的姿势和地面反作用力估计和稳定性分析推断运动员的脑震荡史。

IF 2.6 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
William Alves, Athanasios Babouras, Paul A Martineau, Danielle Schutt, Shawn Robbins, Thomas Fevens
{"title":"利用增强运动录像的姿势和地面反作用力估计和稳定性分析推断运动员的脑震荡史。","authors":"William Alves, Athanasios Babouras, Paul A Martineau, Danielle Schutt, Shawn Robbins, Thomas Fevens","doi":"10.1007/s11517-025-03411-0","DOIUrl":null,"url":null,"abstract":"<p><p>Concussions present a significant risk to athletes, with females exhibiting higher rates and prolonged recovery times than males. Current sideline concussion detection methods, such as the King-Devick test commonly used as a rapid screening tool designed to evaluate eye movement, attention, language, and cognitive processing abilities suffer from validity issues. This is especially true among young athletes highlighting the need for more accurate and objective assessment tools. This study investigates the ability of the Microsoft Kinect V2 pose estimation depth sensor to reliably measure subtle postural stability differences between athletes with a history of concussion and healthy controls. Traditional methods make use of expensive force plates which require trained personnel and controlled environments, limiting their use in resource-limited settings. Inspired by previous research utilizing force plates, our study analyzes video recordings of athletes performing specific exercises to detect dynamic balance deficits. A machine learning approach is employed to predict ground reaction forces from pose estimation video recordings, which are then analyzed to measure time to stabilization. Results reveal significant differences in movement mechanics between concussed and control groups, with the drop vertical jump (DVJ) exercise demonstrating the highest discriminatory power. Notably, concussed individuals exhibit longer time to stabilization (mean difference <math><mo>=</mo></math> 0.089 s, p = 0.046) during DVJ, indicating potential lingering balance impairments. While single-leg squat (SLS) and single-leg hop (SLH) exercises showed fewer discriminatory metrics than DVJ, they still provide valuable insights into balance capabilities. The DVJ yielded the largest statistical difference between injured and healthy male athletes, while the SLH was more effective for females and the SLS, while effective for ACL rehab progress assessment, was equally ineffective for both males and females.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Inferring concussion history in athletes using pose and ground reaction force estimation and stability analysis of plyometric exercise videos.\",\"authors\":\"William Alves, Athanasios Babouras, Paul A Martineau, Danielle Schutt, Shawn Robbins, Thomas Fevens\",\"doi\":\"10.1007/s11517-025-03411-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Concussions present a significant risk to athletes, with females exhibiting higher rates and prolonged recovery times than males. Current sideline concussion detection methods, such as the King-Devick test commonly used as a rapid screening tool designed to evaluate eye movement, attention, language, and cognitive processing abilities suffer from validity issues. This is especially true among young athletes highlighting the need for more accurate and objective assessment tools. This study investigates the ability of the Microsoft Kinect V2 pose estimation depth sensor to reliably measure subtle postural stability differences between athletes with a history of concussion and healthy controls. Traditional methods make use of expensive force plates which require trained personnel and controlled environments, limiting their use in resource-limited settings. Inspired by previous research utilizing force plates, our study analyzes video recordings of athletes performing specific exercises to detect dynamic balance deficits. A machine learning approach is employed to predict ground reaction forces from pose estimation video recordings, which are then analyzed to measure time to stabilization. Results reveal significant differences in movement mechanics between concussed and control groups, with the drop vertical jump (DVJ) exercise demonstrating the highest discriminatory power. Notably, concussed individuals exhibit longer time to stabilization (mean difference <math><mo>=</mo></math> 0.089 s, p = 0.046) during DVJ, indicating potential lingering balance impairments. While single-leg squat (SLS) and single-leg hop (SLH) exercises showed fewer discriminatory metrics than DVJ, they still provide valuable insights into balance capabilities. The DVJ yielded the largest statistical difference between injured and healthy male athletes, while the SLH was more effective for females and the SLS, while effective for ACL rehab progress assessment, was equally ineffective for both males and females.</p>\",\"PeriodicalId\":49840,\"journal\":{\"name\":\"Medical & Biological Engineering & Computing\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2025-07-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Medical & Biological Engineering & Computing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s11517-025-03411-0\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical & Biological Engineering & Computing","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s11517-025-03411-0","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

摘要

脑震荡对运动员来说有很大的风险,女性的发病率比男性高,恢复时间也比男性长。目前的辅助脑震荡检测方法,如King-Devick测试,通常被用作评估眼球运动、注意力、语言和认知处理能力的快速筛查工具,存在有效性问题。在年轻运动员中尤其如此,这突出了对更准确和客观的评估工具的需求。本研究调查了Microsoft Kinect V2姿势估计深度传感器在可靠地测量有脑震荡病史的运动员和健康对照组之间细微姿势稳定性差异的能力。传统方法使用昂贵的测力板,这需要训练有素的人员和受控的环境,限制了它们在资源有限的情况下的使用。受先前利用力板的研究启发,我们的研究分析了运动员进行特定运动的视频记录,以检测动态平衡缺陷。采用机器学习方法从姿态估计视频记录中预测地面反作用力,然后对其进行分析以测量稳定所需的时间。结果显示,脑震荡组和对照组在运动力学方面存在显著差异,其中落差垂直跳(DVJ)运动表现出最高的歧视性力量。值得注意的是,脑震荡个体在DVJ期间表现出更长的稳定时间(平均差异= 0.089 s, p = 0.046),表明潜在的持续性平衡障碍。虽然单腿深蹲(SLS)和单腿跳(SLH)运动比DVJ运动显示出更少的歧视性指标,但它们仍然为平衡能力提供了有价值的见解。DVJ在受伤和健康的男性运动员之间产生了最大的统计差异,而SLH对女性更有效,而SLS虽然对ACL康复进展评估有效,但对男性和女性同样无效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Inferring concussion history in athletes using pose and ground reaction force estimation and stability analysis of plyometric exercise videos.

Concussions present a significant risk to athletes, with females exhibiting higher rates and prolonged recovery times than males. Current sideline concussion detection methods, such as the King-Devick test commonly used as a rapid screening tool designed to evaluate eye movement, attention, language, and cognitive processing abilities suffer from validity issues. This is especially true among young athletes highlighting the need for more accurate and objective assessment tools. This study investigates the ability of the Microsoft Kinect V2 pose estimation depth sensor to reliably measure subtle postural stability differences between athletes with a history of concussion and healthy controls. Traditional methods make use of expensive force plates which require trained personnel and controlled environments, limiting their use in resource-limited settings. Inspired by previous research utilizing force plates, our study analyzes video recordings of athletes performing specific exercises to detect dynamic balance deficits. A machine learning approach is employed to predict ground reaction forces from pose estimation video recordings, which are then analyzed to measure time to stabilization. Results reveal significant differences in movement mechanics between concussed and control groups, with the drop vertical jump (DVJ) exercise demonstrating the highest discriminatory power. Notably, concussed individuals exhibit longer time to stabilization (mean difference = 0.089 s, p = 0.046) during DVJ, indicating potential lingering balance impairments. While single-leg squat (SLS) and single-leg hop (SLH) exercises showed fewer discriminatory metrics than DVJ, they still provide valuable insights into balance capabilities. The DVJ yielded the largest statistical difference between injured and healthy male athletes, while the SLH was more effective for females and the SLS, while effective for ACL rehab progress assessment, was equally ineffective for both males and females.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Medical & Biological Engineering & Computing
Medical & Biological Engineering & Computing 医学-工程:生物医学
CiteScore
6.00
自引率
3.10%
发文量
249
审稿时长
3.5 months
期刊介绍: Founded in 1963, Medical & Biological Engineering & Computing (MBEC) continues to serve the biomedical engineering community, covering the entire spectrum of biomedical and clinical engineering. The journal presents exciting and vital experimental and theoretical developments in biomedical science and technology, and reports on advances in computer-based methodologies in these multidisciplinary subjects. The journal also incorporates new and evolving technologies including cellular engineering and molecular imaging. MBEC publishes original research articles as well as reviews and technical notes. Its Rapid Communications category focuses on material of immediate value to the readership, while the Controversies section provides a forum to exchange views on selected issues, stimulating a vigorous and informed debate in this exciting and high profile field. MBEC is an official journal of the International Federation of Medical and Biological Engineering (IFMBE).
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信