Gait tracking in dogs using DeepLabCut: A markerless machine learning approach for controlled settings
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Bibliographic record
Abstract
Analysing locomotion is critical for assessing canine health and diagnosing musculoskeletal conditions, yet traditional motion capture methods for dog gait analysis remain impractical in many clinical and industry settings. Markerless deep-learning approaches, such as DeepLabCut (DLC), offer a promising alternative, but their performance in gait analysis, particularly across diverse dog breeds, remains largely untested. In addition, the ability to automate aspects of gait parameter extraction from the resulting dataset, an important requirement for industry practitioners, is also widely untested. In this study, we trained a bespoke neural network on a 2100 training frames, for 2D markerless tracking on eight dog breeds and developed a scripted workflow for semi-automated gait parameter extraction. We calculated several temporal and kinematic variables, including duty factor and joint ranges of motion, comparing values of a widely studied breed (Labrador Retrievers) to literature data. Our model’s performance aligned with previous DLC studies, performing strongly on well-defined landmarks (E.g. nose, eye, carpal, tarsal), whilst struggling with less morphologically discrete locations (E.g. shoulder, hip). ANOVA results from our mixed model revealed a significant effect of body part on tracking performance (p = 0.003), yet no significant effect of breed (p = 0.828) and a small interaction effect between breed and body part (p = 0.049). Our semi-automated workflow successfully extracted gait parameters across our study breeds, though performance was highly dependent on the quality of underlying tracking data. Duty factor and stifle range of motion measures from our labradors showed good overlap with literature values, yet the broader distribution in our data highlighted important limitations in cross-study comparisons. These results suggest that a markerless deep-learning approach could provide a viable alternative to traditional motion capture for canine gait analysis, offering potential applications for both clinical and industry settings. • Extracted gait parameters from seven breeds of dog using a custom neural network. • Tracking performance varied significantly by landmark, but not by breed. • Large parts of gait parameter extraction process successfully automated from tracking output. • Our gait parameters were consistent with literature values for a benchmark study breed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it