3-Dimensional pose estimation to detect posture transition in freestall-housed dairy cows

Freestall comfort is reflected in various indicators, including the ability for dairy cattle to display unhindered posture transition movements in the cubicles. ensure farm animal welfare, it is instrumental for the farm management to be able to continuously monitor occurrences of abnormal motions. Advances in computer vision have enabled accurate kinematic measurements in several fields, such as human, equine, and bovine biomechanics. An important step upstream to measuring displacement during posture transitions is determining that the behavior is accurately detected. In this study, we propose a framework for detecting lying-to-standing posture transitions from 3-dimensional (3D) pose estimation data. A multiview computer vision system recorded posture transitions between December 2021 and April 2022 in a Swedish stall housing 183 individual cows. The output data consisted of the 3D coordinates of specific anatomical landmarks. The sensitivity of posture transition detection was 88.2%, and precision reached 99.5%. In analyzing those transition movements, breakpoints detected the timestamp of onset of the rising motion, which was compared with that annotated by observers. Agreement between observers, measured by intraclass correlation, was 0.85 between 3 human observers and 0.81 when adding the automated detection. The intra-observer mean absolute difference in annotated timestamps ranged from 0.4 s to 0.7 s. The mean absolute difference between each observer and the automated detection ranged from 1.0 to 1.3 s. We found a significant difference in annotated timestamps between all observer pairs, but not between the observers and the automated detection, leading to the conclusion that the automated detection does not introduce a distinct bias. We conclude that the model is able to accurately detect the phenomenon of interest and that it is equitable to an observer.