Cattle behavior recognition from accelerometer data: Leveraging in-situ cross-device model learning

Summary

Automating livestock behavior recognition using wearable sensors offers significant benefits for monitoring animal health, ensuring welfare, and enhancing farm productivity. While collar-mounted accelerometers provide useful data leading to accurate behavior recognition models, ear-tags offer greater practicality and scalability. However, ear-tag data is affected by independent ear movements (e.g., for flicking flies), necessitating extensive labeled data for accurate recognition, which is time-consuming and costly to obtain. To address this challenge, we propose a pioneering cross-device learning approach. By leveraging a pre-trained behavior recognition model from collar data to guide ear-tag model training, we significantly reduce the need for manual labeling of ear-tag data. This facil