William Alves, Athanasios Babouras, Paul A Martineau, Danielle Schutt, Shawn Robbins, Thomas Fevens
{"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}
引用次数: 0
Abstract
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.
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).