Stride frequency derived from GPS speed fluctuations in galloping horses
Bibliographic record
Abstract
Changes in gallop stride parameters prior to injury have been documented previously in Thoroughbred racehorses. Validating solutions for quantification of fundamental stride parameters is important for large scale studies investigating injury related factors. This study describes a fast Fourier transformation-based method for extracting stride frequency (SF) values from speed fluctuations recorded with a standalone GPS-logger suitable for galloping horses. Limits of agreement with SF values derived from inertial measurement unit (IMU) pitch data are presented. Twelve Thoroughbred horses were instrumented with a GPS-logger (Vbox sport, Racelogic, 10 Hz samplerate) and a IMU-logger (Xsens DOT, Xsens, 120 Hz samplerate), both attached to the saddlecloth in the midline caudal to the saddle and time synchronized by minimizing root mean square error between differentiated GPS and IMU heading. Each horse performed three gallop trials with a target speed of 36 miles per hour (16.1 ms−1) on a dirt racetrack. Average speed was 16.48 ms−1 ranging from 16.1 to 17.4 ms−1 between horses. Limits of agreement between GPS- and IMU-derived SF had a bias of 0.0032 Hz and a sample-by-sample precision of +/−0.027 Hz calculated over N = 2196 values. The stride length uncertainty related to the trial-by-trial SF precision of 0.0091 Hz achieved across 100 m gallop sections is smaller than the 10 cm decrease in stride length that has been associated with an increased risk of musculoskeletal injury. This suggests that the described method is suitable for calculating fundamental stride parameters in the context of injury prevention in galloping horses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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 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".