Lower-limb Monitoring with IMUs in Sport: A Systematic Review
Bibliographic record
Abstract
Abstract The advancement of wearable technology has enhanced athlete monitoring across various sports and competitive levels. Inertial measurement units (IMUs) enable the quantification of external load at different anatomical locations, providing ecologically valid data in real-world sporting environments. This systematic review examines the prevalence, application, and methodological considerations of lower limb-worn IMUs in competitive sports outside laboratory settings. A comprehensive search across four databases identified 71 relevant studies categorized by publication information, participant demographics, device specifications, and task characteristics. Findings indicate a substantial increase in the use of lower limb IMUs over the past decade, with a wide range of spatiotemporal parameters analyzed across diverse athletic populations. Despite this growth, gaps remain in device specifications, longitudinal studies, and standardization of monitoring protocols. These results highlight the potential of IMUs as a noninvasive tool for spatiotemporal parameters and sport-specific movement patterns, offering valuable insights to refine training prescriptions and optimize athlete performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".