Behavior classification and rhythm analysis of pigs based on wearable sensors
Bibliographic record
Abstract
Assessing the health status of pigs is critical for the swine industry, as the timely detection of abnormal behaviors can improve both production efficiency and animal welfare. To address the current challenges associated with wearable sensor data in pigs, such as indistinct behavioral features, irregular behavior durations, and the difficulty of manual classification, this paper proposes a behavior classification method based on ear tag data to detect three primary behaviors in pigs: feeding, movement, and sleeping. First, we collected 3-D accelerometer data from the ear tags of 40 pigs over a 60-day period. Then, using a sliding window approach, we transformed the accelerometer data for different behaviors into short-time Fourier transform (STFT) spectrograms, time-amplitude graphs, scatter plots, and density plots at various time scales (0.5 seconds, 1 second, 10 seconds, 30 seconds, 60 seconds, 120 seconds, and 180 seconds). Finally, we employed a spatial attention-enhanced ResNeSt-101 model to classify the images representing pig behaviors. The experimental results showed that time-amplitude graphs provided superior behavior classification performance compared to other image types, with the 60-second time-amplitude graphs achieving the highest accuracy at 91.23%. Statistical analysis of pig behavior using ear tag data indicated that the primary feeding times were between 6:00-10:00 and 14:00-18:00, with a total daily feeding duration of approximately 1.5 hours. This study introduces a novel technological approach for pig behavior detection and health assessment, with promising potential to enhance production efficiency and animal welfare.
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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 | 0.000 |
| 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".