The modeling of multiple animals that share behavioral features

TL;DR

This paper introduces a novel set of hierarchical hidden Markov models based on Dirichlet processes to infer and compare behaviors across multiple animals, capturing shared and unique behavioral features.

Contribution

It presents a new modeling framework that allows for shared behavioral parameters among animals and estimates the number of behaviors directly from data.

Findings

Four dogs share most behavior characteristics

Two dogs exhibit specific, distinct behaviors

Model effectively infers shared and individual behaviors

Abstract

In this work, we propose a model that can be used to infer the behavior of multiple animals. Our proposal is defined as a set of hidden Markov models that are based on the sticky hierarchical Dirichlet process, with a shared base-measure, and a STAP emission distribution. The latent classifications are representative of the behavior assumed by the animals, which is described by the STAP parameters. Given the latent classifications, the animals are independent. As a result of the way we formalize the distribution over the STAP parameters, the animals may share, in different behaviors, the set or a subset of the parameters, thereby allowing us to investigate the similarities between them. The hidden Markov models, based on the Dirichlet process, allow us to estimate the number of latent behaviors for each animal, as a model parameter. This proposal is motivated by a real data problem, where the GPS coordinates of six Maremma Sheepdogs have been observed. Among the other results, we show that four dogs share most of the behavior characteristics, while two have specific behaviors.