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

arXiv: https://arxiv.org/abs/2404.18159 · HTML: https://mlsanity.com/paper/2404.18159/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 88 references that are in the map.
- First posted on arXiv 28 Apr 2024.

## Builds on (most cited first, 8 of 8)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels](https://mlsanity.com/paper/1910.13051/) (2019)
- [MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification](https://mlsanity.com/paper/2012.08791/) (2020)
- [catch22: CAnonical Time-series CHaracteristics](https://mlsanity.com/paper/1901.10200/) (2019)
- [sktime: A Unified Interface for Machine Learning with Time Series](https://mlsanity.com/paper/1909.07872/) (2019)
- [Deep Learning-based Cattle Activity Classification Using Joint Time-frequency Data Representation](https://mlsanity.com/paper/2011.03381/) (2020)
- [Multimodal sensor data fusion for in-situ classification of animal behavior using accelerometry and GNSS data](https://mlsanity.com/paper/2206.12078/) (2022)
- [Animal behavior classification via deep learning on embedded systems](https://mlsanity.com/paper/2111.12295/) (2021)

## Built on by (most cited first, 3 of 3)

- [Accelerometer-Based Multivariate Time-Series Dataset for Calf Behavior Classification](https://mlsanity.com/paper/2409.00053/) (2024)
- [Behavior-Specific Filtering for Enhanced Pig Behavior Classification in Precision Livestock Farming](https://mlsanity.com/paper/2507.21021/) (2025)
- [A machine learning framework integrating seed traits and plasma parameters for predicting germination uplift in crops](https://mlsanity.com/paper/2510.23657/) (2025)

## Abstract (arXiv)

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 of ROCKET and Catch22 features to Hand-Crafted features. 30 Irish Holstein Friesian and Jersey pre-weaned calves were monitored using accelerometer sensors allowing for 27.4 hours of annotated behaviors. Additional time-series were computed from the raw X, Y and Z-axis and split into 3-second time windows. ROCKET, Catch22 and Hand-Crafted features were calculated for each time window, and the dataset was then split into the train, validation and test sets. Each set of features was used to train three Machine Learning models (Random Forest, eXtreme Gradient Boosting, and RidgeClassifierCV) to classify six behaviours indicative of pre-weaned calf welfare (drinking milk, grooming, lying, running, walking and other). Models were tuned with the validation set, and the performance of each feature-model combination was evaluated with the test set. The best performance across the three models was obtained with ROCKET [average balanced accuracy +/- standard deviation] (0.70 +/- 0.07), followed by Catch22 (0.69 +/- 0.05), surpassing Hand-Crafted (0.65 +/- 0.034). The best balanced accuracy (0.77) was obtained with ROCKET and RidgeClassifierCV, followed by Catch22 and Random Forest (0.73). Thus, tailoring these approaches for specific behaviours and contexts will be crucial in advancing precision livestock farming and enhancing animal welfare on a larger scale.