  Motor behaviour analysis is essential to biomedical research and clinical
diagnostics as it provides a non-invasive strategy for identifying motor
impairment and its change caused by interventions. State-of-the-art
instrumented movement analysis is time- and cost-intensive, since it requires
placing physical or virtual markers. Besides the effort required for marking
keypoints or annotations necessary for training or finetuning a detector, users
need to know the interesting behaviour beforehand to provide meaningful
keypoints. We introduce unsupervised behaviour analysis and magnification
(uBAM), an automatic deep learning algorithm for analysing behaviour by
discovering and magnifying deviations. A central aspect is unsupervised
learning of posture and behaviour representations to enable an objective
comparison of movement. Besides discovering and quantifying deviations in
behaviour, we also propose a generative model for visually magnifying subtle
behaviour differences directly in a video without requiring a detour via
keypoints or annotations. Essential for this magnification of deviations even
across different individuals is a disentangling of appearance and behaviour.
Evaluations on rodents and human patients with neurological diseases
demonstrate the wide applicability of our approach. Moreover, combining
optogenetic stimulation with our unsupervised behaviour analysis shows its
suitability as a non-invasive diagnostic tool correlating function to brain
plasticity.
