# Behavior-Specific Filtering for Enhanced Pig Behavior Classification in Precision Livestock Farming

arXiv: https://arxiv.org/abs/2507.21021 · HTML: https://mlsanity.com/paper/2507.21021/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 21 references that are in the map.
- First posted on arXiv 28 Jul 2025.

## Builds on (most cited first, 1 of 1)

- [Evaluating ROCKET and Catch22 features for calf behaviour classification from accelerometer data using Machine Learning models](https://mlsanity.com/paper/2404.18159/) (2024)

## Abstract (arXiv)

This study proposes a behavior-specific filtering method to improve behavior classification accuracy in Precision Livestock Farming. While traditional filtering methods, such as wavelet denoising, achieved an accuracy of 91.58%, they apply uniform processing to all behaviors. In contrast, the proposed behavior-specific filtering method combines Wavelet Denoising with a Low Pass Filter, tailored to active and inactive pig behaviors, and achieved a peak accuracy of 94.73%. These results highlight the effectiveness of behavior-specific filtering in enhancing animal behavior monitoring, supporting better health management and farm efficiency.