Literature DB >> 34855257

Automated pose estimation in primates.

Benjamin Y Hayden1, Hyun Soo Park2, Jan Zimmermann1.   

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.
© 2021 Wiley Periodicals LLC.

Entities:  

Keywords:  behavioral tracking; big data; deep learning; primates; rhesus macaque

Mesh:

Year:  2021        PMID: 34855257      PMCID: PMC9160209          DOI: 10.1002/ajp.23348

Source DB:  PubMed          Journal:  Am J Primatol        ISSN: 0275-2565            Impact factor:   3.014


  56 in total

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Journal:  Nat Med       Date:  2016-12-05       Impact factor: 53.440

6.  Fast animal pose estimation using deep neural networks.

Authors:  Talmo D Pereira; Diego E Aldarondo; Lindsay Willmore; Mikhail Kislin; Samuel S-H Wang; Mala Murthy; Joshua W Shaevitz
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7.  Influences of demographic, seasonal, and social factors on automated touchscreen computer use by rhesus monkeys (Macaca mulatta) in a large naturalistic group.

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Authors:  Benjamin R Eisenreich; Benjamin Y Hayden; Jan Zimmermann
Journal:  Sci Rep       Date:  2019-10-21       Impact factor: 4.379

9.  Choice-relevant information transformation along a ventrodorsal axis in the medial prefrontal cortex.

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  3 in total

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2.  Deep-learning based identification, tracking, pose estimation, and behavior classification of interacting primates and mice in complex environments.

Authors:  Markus Marks; Jin Qiuhan; Oliver Sturman; Lukas von Ziegler; Sepp Kollmorgen; Wolfger von der Behrens; Valerio Mante; Johannes Bohacek; Mehmet Fatih Yanik
Journal:  Nat Mach Intell       Date:  2022-04-21

Review 3.  The Promise of Behavioral Tracking Systems for Advancing Primate Animal Welfare.

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  3 in total

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