Predicting dairy cow locomotion ability based on kinematic 3D coordinates
Bibliographic record
Abstract
• Early detection of gait abnormality is needed to prevent clinical cases of lameness. • Data augmentation was used to expand the sample size and create a balanced data set. • LSTM models were trained to predict dairy cow gait scores using 3D kinematic data. • Zero-mean, unit-standard deviation normalization outperformed min-max scaling. • The best model achieved a 0.96 accuracy, precision, recall, and F1 score. Our study addresses the challenge of early lameness detection in dairy cows. Traditional visual scoring methods, while non-invasive and cost-effective, require extensive training and are impractical for continuous monitoring. Our research proposes an automated alternative using kinematic data and machine learning. Kinematic data were collected multiple times from 12 Holstein dairy cows over four weeks. After data cleaning, a total of 73 passages were available for model training. A trained observer scored the gait of each passage using a numerical rating system (NRS) ranging from 1 (sound cow) to 5 (severe lame cow) with 0.5 intervals. Data augmentation was used to obtain balanced data sets by adding 1 %, 2.5 %, 5 %, 7.5 %, and 10 % Gaussian noise along with random shifting and followed by two data normalization strategies. The augmented data was split into training (75 %) and testing (25 %) sets. A long short-term memory neural network was trained and evaluated. The highest accuracy, precision, recall, and F1 score achieved on the test set was 0.96 (SD = 0.03) for all metrics. Models trained with data normalized to a mean of zero and standard deviation of one outperformed those using normalization to a range between zero and one. Future research should focus on expanding the range of locomotion scores, particularly covering the early stages of locomotion changes. This is necessary to enable earlier identification and treatment of cows with impaired locomotion ability before they develop lameness.
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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 | 0.000 | 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".