Behavior Classification

Behavior classification research aims to automatically categorize animal and human actions from various data sources, such as video, accelerometry, and touchscreen interactions, primarily using machine learning. Current efforts focus on developing robust and efficient models, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformer architectures, often incorporating techniques like self-supervised learning and pose estimation to improve accuracy and reduce reliance on labeled data. This field is crucial for advancing diverse areas, including animal welfare monitoring, biomedical research (e.g., Alzheimer's disease), and security applications (e.g., detecting dangerous student behavior), by enabling large-scale, objective behavioral analysis.