5 papers
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…