The use of inertial measurement units for analyzing change of direction movement in sports: A scoping review
Bibliographic record
Abstract
Change of direction movement is common in sports and the ability to perform this complex movement efficiently is related to athlete's performance. Wearable devices have been used to evaluate aspects of change of direction movement, but so far there are no clear recommendations on specific metrics to be used. The aims of this scoping review were to evaluate the reliability and validity of inertial measurement unit sensors to provide information on change of direction movement and to summarize the available evidence on inertial measurement units in analyzing change of direction movement in sports. A systematic search was employed in MEDLINE (Ovid), CINAHL (EBSCO host), SPORTDiscus (EBSCO host), EMBASE and Cochrane Database of Systematic Reviews and Web of Science to identify eligible studies. A complementary grey literature search was employed to locate non-peer reviewed studies. The risk of bias of the studies evaluating validity and/or reliability was evaluated using the AXIS tool. The initial search identified 15,165 studies. After duplicate removal and full-text screening 49 studies met the inclusion criteria, with 11 studies evaluating validity and/or reliability. There are promising results on the validity and reliability, but the number of studies is still small and the quality of the studies is limited. Most of the studies were conducted with pre-planned movements and participants were usually adult males. Varying sensor locations limits the ability to generalize these findings. Inertial measurement units (IMU) can be used to detect change of direction (COD) movements and COD heading angles with acceptable validity, but IMU measured or derived kinetic or kinematic variables present inconsistency and over-estimation. Studies can be improved with larger sample sizes and agreement on the metrics used and sensor placement. Future research should include more on-field studies.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".