Le Flao Enora, Siegmund Gunter P, Lenetsky Seth, Borotkanics Robert
The Ohio State University, Columbus, OH, USA.
Auckland University of Technology, Auckland, New Zealand.
Ann Biomed Eng. 2025 Mar;53(3):658-672. doi: 10.1007/s10439-024-03647-1. Epub 2024 Dec 3.
When used in-vivo or in biofidelic environments, many head impact sensors have shown limitations related to the quality and validity of the kinematics measured. The objectives were to assess the quality of kinematic traces from three head impact sensors, determine the effects of signal quality on peak accelerations, and compare measurements across sensors.
Head impacts were collected with instrumented mouthguards, skin patches, and headgear patches during boxing sparring. The quality of the raw kinematic traces for 442 events for each sensor was categorized using pre-defined objective criteria into high, questionable, and low-quality classes. The proportion of high-quality recordings was analyzed by participant, type of impact, and impact location. Associations between signal quality and peak kinematics were assessed within each sensor, and peak kinematics (resolved to the head center of gravity) were compared between sensors.
High-quality criteria were met in 53%, 20%, and 26% of events for the mouthguard, skin patch, and headgear patch, respectively. High-quality recordings were less frequent for impacts occurring close to the sensor (e.g., 30% vs. 61% for the mouthguard) and showed lower peak kinematics than low-quality recordings (p < 0.001). Despite careful selection of high-quality simultaneous recordings, there was little to no association between the sensors' measurements (Spearman's p ≥ 0.043).
The kinematic data often reflected the motion of the sensor itself rather than the motion of the head, overestimating head impact magnitude. Researchers should evaluate data quality prior to analyzing kinematics or injury severity metrics. Comparison of data across studies or in relation to injury risk functions needs to be done with caution when data were acquired from different sensors.
当在体内或生物逼真环境中使用时,许多头部撞击传感器在测量运动学的质量和有效性方面表现出局限性。目的是评估来自三种头部撞击传感器的运动学轨迹质量,确定信号质量对峰值加速度的影响,并比较不同传感器的测量结果。
在拳击对练期间,使用装有仪器的护齿、皮肤贴片和头盔贴片收集头部撞击数据。每个传感器的442个事件的原始运动学轨迹质量,根据预定义的客观标准分为高质量、有问题和低质量类别。通过参与者、撞击类型和撞击位置分析高质量记录的比例。在每个传感器内评估信号质量与峰值运动学之间的关联,并比较不同传感器之间的峰值运动学(分解至头部重心)。
护齿、皮肤贴片和头盔贴片事件分别有53%、20%和26%符合高质量标准。靠近传感器发生的撞击,高质量记录较少(例如,护齿为30%对61%),且峰值运动学低于低质量记录(p<0.001)。尽管精心选择了高质量的同步记录,但传感器测量结果之间几乎没有关联(斯皮尔曼p≥0.043)。
运动学数据通常反映的是传感器本身的运动,而非头部的运动,高估了头部撞击的幅度。研究人员在分析运动学或损伤严重程度指标之前,应评估数据质量。当从不同传感器获取数据时,跨研究或与损伤风险函数相关的数据比较需谨慎进行。