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ABNet: AI-Empowered Abnormal Action Recognition Method for Laboratory Mouse Behavior.

Yuming Chen1, Chaopeng Guo1, Yue Han2

  • 1Software College, Northeastern University, Shenyang 110169, China.

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|September 27, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces ABNet, an AI approach for recognizing abnormal mouse behavior. ABNet uses an enhanced Spatio-Temporal Graph Convolutional Network (ST-GCN) and unsupervised clustering to effectively detect unusual actions in mice.

Keywords:
action recognitioncomputer visionmicemouse behaviorsemi-supervised learning

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Area of Science:

  • Neuroscience
  • Pharmacology
  • Toxicology

Background:

  • Automatic recognition of abnormal mouse behavior is vital for research.
  • Defining abnormal behavior and collecting training data are significant challenges.
  • Existing behavior recognition methods struggle with direct abnormal behavior identification.

Purpose of the Study:

  • To propose ABNet, an AI-powered approach for abnormal action recognition in mice.
  • To enhance feature extraction and encoding for spatio-temporal data.
  • To accurately identify abnormal mouse behaviors using AI.

Main Methods:

  • ABNet employs an enhanced Spatio-Temporal Graph Convolutional Network (ST-GCN) as an encoder.
  • The ST-GCN combines graph and temporal convolutions for spatio-temporal feature analysis.
  • The model is trained on normal behavior samples and uses unsupervised clustering to detect anomalies.

Main Results:

  • ABNet demonstrated strong performance on the Kinetics-Skeleton dataset (32.7% top-1, 55.2% top-5 accuracy).
  • In mouse behavior analysis, ABNet achieved an average accuracy of 83.1% for abnormal motion detection.
  • The enhanced ST-GCN significantly improved feature extraction and encoding capabilities.

Conclusions:

  • ABNet offers an effective solution for abnormal behavior recognition in mice.
  • The AI-empowered approach addresses limitations in traditional behavior analysis.
  • This method advances quantitative analysis in neuroscience, pharmacology, and toxicology research.