Paper

A Workflow for High Through-Put, High Precision Livestock Diagnostic Screening of Locomotor Kinematics

Aerts, Peter, Mielke, Falk, Chris Van Ginneken

2022

Overview

Locomotor kinematics have been challenging inputs for automated diagnostic screening of livestock. Locomotion is a highly variable behavior, and influenced by subject characteristics (e.g. body mass, size, age, disease). We assemble a set of methods from different scientific disciplines, composing an automatic, high through-put workflow which can disentangle behavioral complexity and generate precise individual indicators of non-normal behavior for application in diagnostics and research. For this study, piglets (/Sus domesticus/) were filmed from lateral perspective during their first ten hours of life, an age at which maturation is quick and body mass and size have major consequences for survival. We then apply deep learning methods for point digitization, calculate joint angle profiles, and apply information-preserving transformations to retrieve a multivariate kinematic data set. We train probabilistic models to infer subject characteristics from kinematics. Model accuracy is validated for strides from piglets of normal birth weight (i.e. the category it was trained on), but the models infer the body mass and size of low birth weight piglets (which were left out of training, out-of-sample inference) to be \"normal\". The age of some (but not all) low birth weight individuals is underestimated, indicating developmental delay. Such individuals could be identified automatically, inspected, and treated accordingly. This workflow has potential for automatic, precise screening in livestock management.Competing Interest StatementThe authors have declared no competing interest.Footnotes* The data and results have been re-worked (with minor technical changes) and the manuscript reflected to adjust this. There was an error in the data table, which affected the ankle and wrist joints, but results of the modeling are in overall agreement with the previous submission. The Intro and Discussion were revised for submission in a veterinary-focused journal. Methods were adjusted slightly to facilitate understanding for the new target audience. The previous review did suggest a major revision, including a split of the MS into a methods paper and a piglet-focused paper, which we found unjustified and therefore retracted the submission.* https://git.sr.ht/~falk/piglet_fcas

• Publisher: Cold Spring Harbor Laboratory Press
• Subject: Age / Birth weight / Body mass / Body size / Coordination / Deep learning / Gait / Kinematics / Locomotion / Low-birth-weight / Posture