Evaluating ROCKET and Catch22 features for calf behaviour classification from accelerometer data using Machine Learning models

arXiv:2404.18159v1 Announce Type: new
Abstract: Monitoring calf behaviour continuously would be beneficial to identify routine practices (e.g., weaning, dehorning, etc.) that impact calf welfare in dairy farms. In that regard, accelerometer data collected from neck collars can be used along with Machine Learning models to classify calf behaviour automatically. Hand-crafted features are commonly used in Machine Learning models, while ROCKET and Catch22 features are specifically designed for time-series classification problems in related fields. This study aims to compare the performance …

abstract arxiv classification cs.lg data etc farms features identify impact machine machine learning machine learning models monitoring practices regard type welfare