Automated pose estimation in primates

Research output: Contribution to journalReview articlepeer-review

3 Scopus citations

Abstract

Understanding the behavior of primates is important for primatology, for psychology, and for biology more broadly. It is also important for biomedicine, where primates are an important model organism, and whose behavior is often an important variable of interest. Our ability to rigorously quantify behavior has, however, long been limited. On one hand, we can rigorously quantify low-information measures like preference, looking time, and reaction time; on the other, we can use more gestalt measures like behavioral categories tracked via ethogram, but at high cost and with high variability. Recent technological advances have led to a major revolution in behavioral measurement that offers affordable and scalable rigor. Specifically, digital video cameras and automated pose tracking software can provide measures of full-body position (i.e., pose) of primates over time (i.e., behavior) with high spatial and temporal resolution. Pose-tracking technology in turn can be used to infer behavioral states, such as eating, sleeping, and mating. We call this technological approach behavioral imaging. In this review, we situate the behavioral imaging revolution in the history of the study of behavior, argue for investment in and development of analytical and research techniques that can profit from the advent of the era of big behavior, and propose that primate centers and zoos will take on a more central role in relevant fields of research than they have in the past.

Original languageEnglish (US)
JournalAmerican journal of primatology
DOIs
StateAccepted/In press - 2021

Bibliographical note

Publisher Copyright:
© 2021 Wiley Periodicals LLC

Keywords

  • behavioral tracking
  • big data
  • deep learning
  • primates
  • rhesus macaque

PubMed: MeSH publication types

  • Journal Article
  • Review

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