Work

ActBeCalf: Accelerometer-based multivariate time-series dataset for calf behavior classification

Year: 2025

Type: data-paper

Abstract: to address this challenge. Thirty pre-weaned dairy calves (Holstein Friesian and Jersey) housed in 4 group pens at Teagasc Moorepark Research Farm (Fermoy, Co. Cork, Ireland) were equipped with a 3D-accelerometer sensor (sampling rate: 25 Hz) attached to a neck-collar from one week of birth for 13 weeks. The calves were simultaneously filmed with a high-up camera in each pen. Every 15 days, accelerometers were removed from the collars to recharge the battery, collect the data, and reattached to the neck collar again. At the end of the trial, behaviors were manually annotated from the videos using the Behavioral Observation Research Interactive Software (BORIS) by three observers (Cohen's Kappa = 0.72 ± 0.01) using an ethogram with 23 pre-weaned dairy calves' behaviors. Videos were carefully selected to annotate calves' behaviors. Observations were synchronized with the accelerometer timestamps using an external clock and aligned to the corresponding accelerometer time series. Thereby, ActBeCalf contains 27.4 h of accelerometer data from 30 pre-weaned calves (age 23.7 ± 10.7 days) aligned adequately with calf behaviors. The dataset includes the main behaviors of the calf time-budget, like lying, standing, walking, and running, as well as less prominent behaviors, such as sniffing, scratching, social interaction, and grooming. The reliability of ActBeCalf was validated by developing two machine learning models designed to classify behaviors into two and four classes, respectively. Good predictive performance was achieved for both models (balanced accuracy: 92 % and 84 %, respectively), thereby confirming ActBeCalf's reliability for model development in the field. The code utilized for the classification is publicly available in the dataset repository. ActBeCalf is a comprehensive, ready-to-use dataset, ideal for advancing research in two key areas: classifying pre-weaned calf behavior to support animal welfare initiatives and developing innovative time-series classification models in machine learning.

Source: Data in Brief

DOI: 10.1016/j.dib.2025.111462

Authors: Oshana Iddi Dissanayake, Sarah E. McPherson, Joseph Allyndrée, Emer Kennedy, Pádraig Cunningham, Lucile Riaboff