Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada.
School of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada.
Ann Biomed Eng. 2024 Oct;52(10):2666-2677. doi: 10.1007/s10439-024-03592-z. Epub 2024 Aug 3.
Instrumented mouthguards (iMGs) are widely applied to measure head acceleration event (HAE) exposure in sports. Despite laboratory validation, on-field factors including potential sensor skull-decoupling and spurious recordings limit data accuracy. Video analysis can provide complementary information to verify sensor data but lacks quantitative kinematics reference information and suffers from subjectivity. The purpose of this study was to develop a rigorous multi-stage screening procedure, combining iMG and video as independent measurements, aimed at improving the quality of on-field HAE exposure measurements. We deployed iMGs and gathered video recordings in a complete university men's ice hockey varsity season. We developed a four-stage process that involves independent video and sensor data collection (Stage I), general screening (Stage II), cross verification (Stage III), and coupling verification (Stage IV). Stage I yielded 24,596 iMG acceleration events (AEs) and 17,098 potential video HAEs from all games. Approximately 2.5% of iMG AEs were categorized as cross-verified and coupled iMG HAEs after Stage IV, and less than 1/5 of confirmed or probable video HAEs were cross-verified with iMG data during stage III. From Stage I to IV, we observed lower peak kinematics (median peak linear acceleration from 36.0 to 10.9 g; median peak angular acceleration from 3922 to 942 rad/s) and reduced high-frequency signals, indicative of potential reduction in kinematic noise. Our study proposes a rigorous process for on-field data screening and provides quantitative evidence of data quality improvements using this process. Ensuring data quality is critical in further investigation of potential brain injury risk using HAE exposure data.
仪器化口腔防护器（iMGs）广泛应用于测量运动中的头部加速事件（HAE）暴露。尽管经过了实验室验证，但包括潜在传感器颅骨解耦和虚假记录在内的现场因素限制了数据的准确性。视频分析可以提供补充信息来验证传感器数据，但缺乏定量运动学参考信息，并存在主观性。本研究的目的是开发一种严格的多阶段筛选程序，将 iMG 和视频作为独立的测量方法相结合，旨在提高现场 HAE 暴露测量的质量。我们在一个完整的大学男子冰球校队赛季中部署了 iMG 并收集了视频记录。我们开发了一个四阶段的过程，包括独立的视频和传感器数据收集（第 I 阶段）、一般筛选（第 II 阶段）、交叉验证（第 III 阶段）和耦合验证（第 IV 阶段）。第 I 阶段从所有比赛中产生了 24596 个 iMG 加速度事件（AEs）和 17098 个潜在视频 HAE。经过第 IV 阶段后，约有 2.5%的 iMG AEs 被归类为交叉验证和耦合 iMG HAE，而在第 III 阶段，不到 1/5 的确认或可能的视频 HAE 与 iMG 数据交叉验证。从第 I 阶段到第 IV 阶段，我们观察到较低的峰值运动学（中位数峰值线性加速度从 36.0 降至 10.9 g；中位数峰值角加速度从 3922 降至 942 rad/s）和减少的高频信号，表明运动学噪声可能降低。我们的研究提出了一种严格的现场数据筛选过程，并提供了使用该过程提高数据质量的定量证据。确保数据质量对于使用 HAE 暴露数据进一步研究潜在的脑损伤风险至关重要。
