DOI: 10.1002/ame2.12530
High-throughput markerless pose estimation and home-cage activity analysis of tree shrew using deep learning
Yangzhen Wang
Feng Su
Rixu Cong
Mengna Liu
Kaichen Shan
Xiaying Li
Desheng Zhu
Yusheng Wei
Jiejie Dai
Chen Zhang
Yonglu Tian
Abstract：Background: Quantifying the rich home-cage activities of tree shrews provides a reliable basis for understanding their daily routines and building disease models. However, due to the lack of effective behavioral methods, most efforts on tree shrew behavior are limited to simple measures, resulting in the loss of much behavioral information.
Methods: To address this issue, we present a deep learning (DL) approach to achieve markerless pose estimation and recognize multiple spontaneous behaviors of tree shrews, including drinking, eating, resting, and staying in the dark house, etc.
Results: This high-throughput approach can monitor the home-cage activities of 16 tree shrews simultaneously over an extended period. Additionally, we demonstrated an innovative system with reliable apparatus, paradigms, and analysis methods for investigating food grasping behavior. The median duration for each bout of grasping was 0.20s.
Conclusion: This study provides an efficient tool for quantifying and understand tree shrews' natural behaviors.
Keywords：deep learningfood graspinghome-cage activitypose estimationtree shrew
Publication Date：2025-03-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：10( 896-905 )