Unsupervised Behaviour Analysis and Magnification (uBAM) using Deep Learning

TL;DR

uBAM is an unsupervised deep learning method that analyzes and magnifies subtle behavioral deviations in videos, aiding biomedical research and diagnostics without manual annotations.

Contribution

The paper introduces a novel unsupervised deep learning framework for behavior analysis and magnification that does not require keypoints or annotations, enabling objective and scalable movement comparison.

Findings

Effective in analyzing rodent and human neurological behaviors

Able to magnify subtle behavioral deviations visually

Applicable as a non-invasive diagnostic tool

Abstract

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.