Unsupervised Behaviour Analysis and Magnification (uBAM) using Deep Learning
This work provides a non-invasive, automatic diagnostic tool for biomedical research and clinical diagnostics, particularly for identifying motor impairment and its changes, reducing the time and cost associated with traditional instrumented movement analysis.
The paper introduces unsupervised behaviour analysis and magnification (uBAM), a deep learning algorithm that automatically discovers and magnifies deviations in motor behaviour without requiring markers or prior knowledge of interesting behaviours. It achieves this by unsupervised learning of posture and behaviour representations and disentangling appearance from behaviour to magnify subtle differences directly in video.
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.