Technical note: Use of accelerometers to describe gait patterns in dairy calves
Bibliographic record
Abstract
Developments in accelerometer technology offer new opportunities for automatic monitoring of animal behavior. Until now, commercially available accelerometers have been used to measure walking in adult cows but have failed to identify walking in calves. We described the pattern of acceleration associated with various gaits in calves and tested whether measures of acceleration could be used to count steps and distinguish among gait types. A triaxial accelerometer (sampling at 33 readings/s with maximum measurement at +/-3.2 g) was attached to 1 hind leg of 7 dairy calves, and each calf was walked to a familiar large arena (29.1 x 4.8m) and encouraged to walk and run for 8 to 10 min while being video recorded. The video recordings were watched in slow motion and a total of 54 recordings of 3 to 6s duration of either galloping (n=21), trotting (n=13), or walking (n=21) were identified and the number of steps were counted. Accelerometer data was then analyzed for each gait. Steps could be clearly identified by changes in the acceleration in the forward and vertical axes and vector sum, but less clearly in the lateral axis. The number of steps counted using the forward axis was highly correlated with the number observed from the video recordings. Galloping, trotting, and walking differed significantly in the median interpeak intervals in acceleration in the forward axis and in the vector sum of the acceleration in the 2 axes. Interpeak intervals could be used to discriminate among the 3 gaits, although walking was most clearly distinguished from galloping. Automated measures of acceleration of the leg in the forward and vertical dimensions can be used to count steps and classify gaits of calves.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".