Variation of Kinematic Metrics With Perceived Fatigue in Ice Skating Measured Using Wearable Sensors
Bibliographic record
Abstract
ABSTRACT: 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 39(10): e1178-e1187, 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".