ArticleAnimal models and experimental medicine2025
High-throughput markerless pose estimation and home-cage activity analysis of tree shrew using deep learning.
Article in Animal models and experimental medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Artificial Intelligence-Based Decoding of Animal Micro-Expressions: A Review of Methodological Advances and Translational Applications.Animals : an open access journal from MDPI · 2026Review
- High-throughput markerless pose estimation and home-cage activity analysis of tree shrew using deep learning.Animal models and experimental medicine · 2025Article
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Authors and funding
11 authors.
Funding
Abstract
backgroundQuantifying 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.
methodsTo 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.
resultsThis 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 0.20 s.
conclusionThis study provides an efficient tool for quantifying and understand tree shrews' natural behaviors.
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