Khandan Aminreza, Fathian Ramin, Carey Jason P, Rouhani Hossein
Department of Mechanical Engineering, University of Alberta, Edmonton, Alberta, Canada.
J Strength Cond Res. 2025 Jun 24. doi: 10.1519/JSC.0000000000005181.
Khandan, A, Fathian, R, Carey, JP, and Rouhani, H. Variation of kinematic metrics with perceived fatigue in ice skating measured using wearable sensors. J Strength Cond Res XX(X): 000-000, 2025-Enabling to obtain ice skaters' kinematics, wearable technology can track skaters' performance and thus detect performance fatigue in real-world settings. Therefore, this study aimed to investigate the potential of wearable inertial measurement units (IMU) to track skaters' performance, predict perceived fatigue, and detect severe fatigue onset before serious fatigue-related sequelae. In a multistage aerobic experiment, 19 subjects, 2 groups of high- and low-caliber skaters clustered by a novel algorithm, were asked to skate at a self-selected speed around an ice rink. During the experiments, subjects skated with 2 IMUs on their dominant leg's shank and thigh and 4 IMUs on their skates, pelvis, and trunk. These IMU outputs were used to develop 22 kinematic metrics whose variations were monitored with self-reported perceived fatigue by a linear mixed model, considering the effect of caliber. Finally, a machine learning algorithm was implemented to predict severe fatigue onset using the proposed kinematic metrics. The variations of intersegment correlation, joint angle fluctuations, and trunk angle were considerable (6-17% variation) during this intermittent skating experiment. In addition, a gradient-boosting model could predict severe fatigue onset with average precision, sensitivity, accuracy, and F1 score of 75, 81, 74, and 78%, respectively, in 196 skating stages captured from the subjects. The proposed kinematic metrics, as performance indicators, could also indicate perceived fatigue during an aerobic ice skating experiment and predict severe fatigue onset. The kinematic metrics introduced in this study equip coaches with quantitative tools to monitor performance and assess perceived fatigue in ice skating.
汗丹、A、法蒂安、R、凯里、JP和鲁哈尼、H。使用可穿戴传感器测量花样滑冰中运动学指标随感知疲劳的变化。《力量与体能研究杂志》XX(X): 000 - 000,2025年——可穿戴技术能够获取花样滑冰运动员的运动学数据,从而追踪运动员的表现,进而在实际场景中检测表现疲劳。因此,本研究旨在探讨可穿戴惯性测量单元(IMU)追踪花样滑冰运动员表现、预测感知疲劳以及在严重疲劳相关后遗症出现之前检测严重疲劳发作的潜力。在一项多阶段有氧运动实验中,19名受试者,通过一种新算法分为两组,即高水准和低水准花样滑冰运动员,被要求以自选速度在溜冰场滑行。在实验过程中,受试者在其优势腿的小腿和大腿上佩戴2个IMU,在冰鞋、骨盆和躯干上佩戴4个IMU。这些IMU输出数据被用于开发22个运动学指标,通过线性混合模型,考虑水准因素,用自我报告的感知疲劳来监测这些指标的变化。最后,实施一种机器学习算法,使用所提出的运动学指标预测严重疲劳发作。在这次间歇滑冰实验中,节段间相关性、关节角度波动和躯干角度的变化相当大(变化6 - 17%)。此外,在从受试者获取的196个滑冰阶段中,梯度提升模型能够分别以75%、81%、74%和78%的平均精度、灵敏度、准确率和F1分数预测严重疲劳发作。所提出的运动学指标作为表现指标,也能够在有氧花样滑冰实验中指示感知疲劳并预测严重疲劳发作。本研究中引入的运动学指标为教练提供了定量工具,以监测花样滑冰中的表现并评估感知疲劳。