Fig. 1
From: Deep phenotyping reveals movement phenotypes in mouse neurodevelopmental models
Processing of video recordings in the open field produces multi-scale quantitative descriptions of behavior. The pipeline takes virtual markers from pose estimation with LEAP to find behavior clusters and generate wavelet signatures. I. A visual representation of the embedding/clustering steps. Distance matrix calculated between virtual markers per each time frame transforms into the frequency domain and clustered using k-means. II. Raw joint trajectories are used to create wavelet signatures or ’behavioral fingerprints’ by finding the mean power spectrum during each behavioral cluster found in part I