Unsupervised decomposition of natural monkey behavior into a sequence of motion motifs.

ABSTRACT: Nonhuman primates (NHPs) exhibit complex and diverse behavior that typifies advanced cognitive function and social communication, but quantitative and systematical measure of this natural nonverbal processing has been a technical challenge. Specifically, a method is required to automatically segment time series of behavior into elemental motion motifs, much like finding meaningful words in character strings. Here, we propose a solution called SyntacticMotionParser (SMP), a general-purpose unsupervised behavior parsing algorithm using a nonparametric Bayesian model. Using three-dimensional posture-tracking data from NHPs, SMP automatically outputs an optimized sequence of latent motion motifs classified into the most likely number of states. When applied to behavioral datasets from common m

SUBMITTER: Mimura K 

PROVIDER: S-EPMC11371840 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature