7 papers
EweAcT: Ewe behaviour aligned to accelerometer data for activity monitoring in extensive grazing systems
Lucile Riaboff, Ny Aina Andriamampandry, Jean-François Bompa +14
Monitoring livestock behaviour under extensive conditions would provide valuable insights to assess animal adaption to environmental perturbations in agroecological systems (e.g.,…
Genetic determinism of circadian rhythm of feed intake and relation with feed efficiency evaluated in group-housed growing Large White pigs
Lucile Riaboff, Ingrid David
Background Genetic parameters of feeding behaviours traits from electronic feeding stations in relation to feed efficiency have been widely explored. However, genetic determinism o…
A Comparison of Deep Learning and Established Methods for Calf Behaviour Monitoring
Oshana Dissanayake, Lucile Riaboff, Sarah E. McPherson +2
In recent years, there has been considerable progress in research on human activity recognition using data from wearable sensors. This technology also has potential in the context…
Accelerometer-Based Multivariate Time-Series Dataset for Calf Behavior Classification
Oshana Dissanayake, Sarah E. McPherson, Joseph Allyndree +3
Getting new insights on pre-weaned calf behavioral adaptation to routine challenges (transport, group relocation, etc.) and diseases (respiratory diseases, diarrhea, etc.) is a pro…
Classifying active and inactive states of growing rabbits from accelerometer data using machine learning algorithms
Mónica Mora, Lucile Riaboff, Ingrid David +2
This study explores how wearable accelerometers, small devices that measure acceleration, can help monitor the activity of growing rabbits. We equipped 16 rabbits with these device…
Development of a digital tool for monitoring the behaviour of pre-weaned calves using accelerometer neck-collars
Oshana Dissanayake, Sarah E. Mcpherson, Joseph Allyndrée +3
Automatic monitoring of calf behaviour is a promising way of assessing animal welfare from their first week on farms. This study aims to (i) develop machine learning models from ac…
Evaluating ROCKET and Catch22 features for calf behaviour classification from accelerometer data using Machine Learning models
Oshana Dissanayake, Sarah E. McPherson, Joseph Allyndree +3
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, a…