Preliminary Development of a Database for Detecting Active Mounting Behaviors Using Signals Acquired from IoT Collars in Free-Grazing Cattle.

This study presents the development of a database for detecting active mounts, utilizing IoT collars equipped with Inertial Measurement Units (IMUs) installed on eight Holstein Friesian cows, along with video recordings from a long-range PTZ camera mounted in a central pole for event labeling in the natural environment when free grazing. The resulting database comprises 415 labeled events associated with various behaviors, containing acceleration signals in both the Body and World Frame of reference and gyroscope signals. A Support Vector Machine (SVM) algorithm is implemented to evaluate the effectiveness of the dataset in detecting active mounts and to compare training performance using both frames. The algorithm achieves an average F1 Score of 88.6% for the World Frame of reference, showing a significant improvement compared to the algorithm trained with Body Frame (78.6%) when both are trained with the same 112 features. After applying feature selection using Sequential Backward Selection (SBS), the SVM exhibits a minor performance difference between the F1 Score obtained with the two reference frames (89.7% World Frame vs. 91.5% Body Frame). This study provides a public dataset and a replicable methodology, facilitating future research on movement-based behavior classification in cattle.