Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics.

ABSTRACT: Keypoint tracking algorithms have revolutionized the analysis of animal behavior, enabling investigators to flexibly quantify behavioral dynamics from conventional video recordings obtained in a wide variety of settings. However, it remains unclear how to parse continuous keypoint data into the modules out of which behavior is organized. This challenge is particularly acute because keypoint data is susceptible to high frequency jitter that clustering algorithms can mistake for transitions between behavioral modules. Here we present keypoint-MoSeq, a machine learning-based platform for identifying behavioral modules ("syllables") from keypoint data without human supervision. Keypoint-MoSeq uses a generative model to distinguish keypoint noise from behavior, enabling it to effectively identi

SUBMITTER: Weinreb C 

PROVIDER: S-EPMC10055085 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature