  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.
