Alves William, Babouras Athanasios, Martineau Paul A, Schutt Danielle, Robbins Shawn, Fevens Thomas
Computer Science and Software Engineering, Concordia University, Montréal, Québec, H3G 1M8, Canada.
Experimental Surgery, McGill University, Montréal, Québec, H3A 0G4, Canada.
Med Biol Eng Comput. 2025 Jul 9. doi: 10.1007/s11517-025-03411-0.
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.
脑震荡对运动员构成重大风险，女性的发病率和恢复时间比男性更长。目前的场边脑震荡检测方法，如常用作快速筛查工具的King-Devick测试，旨在评估眼球运动、注意力、语言和认知处理能力，但存在有效性问题。在年轻运动员中尤其如此，这凸显了对更准确、客观评估工具的需求。本研究调查了微软Kinect V2姿态估计深度传感器可靠测量有脑震荡病史的运动员与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，需要训练有素的人员和受控环境，限制了它们在资源有限环境中的使用。受先前利用测力板进行的研究启发，我们的研究分析了运动员进行特定练习的视频记录，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时间。结果显示，脑震荡组和对照组之间的运动力学存在显著差异，垂直跳练习的鉴别力最高。值得注意的是，在垂直跳过程中，脑震荡患者的稳定时间更长（平均差异0.089秒，p = 0.046），表明可能存在持续的平衡损伤。虽然单腿深蹲和单腿跳练习的鉴别指标比垂直跳少，但它们仍能为平衡能力提供有价值的见解。垂直跳在受伤和健康男性运动员之间产生的统计差异最大，而单腿跳对女性更有效，单腿深蹲虽然对评估前交叉韧带康复进展有效，但对男性和女性同样无效。
