Detecting heat events in dairy cows using accelerometers and unsupervised learning

This study was conducted to investigate the detection of heat events in pasture-based dairy cows fitted with on-animal sensors using unsupervised learning. Accelerometer data from the cow collars were used to identify increased activity levels in cows associated with recorded heat events. Time series data from the accelerometers were first segmented into windows before features were extracted. K-means clustering algorithm was then applied across the windows for grouping. The groups were labelled in terms of their activity intensity: high, medium and low. An activity index level (AIxL) was then derived from a count of activity intensity labels over time. Change detection techniques were then applied on AIxL to find very high activity events. Detected events in AIxL were compared with recorded heat events and observed significant associations between the increased activities through high AIxL values and the observed heat events. We achieved overall accuracy of 82-100% with 100% sensitivity when change detection technique is applied to activity index level.