DISSeCT: An unsupervised framework for high-resolution mapping of rodent behavior using inertial sensors.

ABSTRACT: Decomposing behavior into elementary components remains a central challenge in computational neuroethology. The current standard in laboratory animals involves multi-view video tracking, which, while providing unparalleled access to full-body kinematics, imposes environmental constraints, is data-intensive, and has limited scalability. We present an alternative approach using inertial sensors, which capture high-resolution, environment-independent, compact 3D kinematic data, and are commonly integrated into rodent neurophysiological devices. Our analysis pipeline leverages unsupervised, computationally efficient change-point detection to break down inertial time series into variable-length, statistically homogeneous segments. These segments are then grouped into candidate behavioral motifs

SUBMITTER: Fayat R 

PROVIDER: S-EPMC12527166 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature