Automated analysis of feline posture using deep learning and geometric modeling

Introduction Body posture provides clinically relevant information in cats, but behavioral interpretation can be challenging because postural signs are often subtle, context-dependent, and subject to inter-observer variability. We developed and evaluated a non-invasive computer vision framework for extracting clinically interpretable feline posture descriptors from side-view RGB images acquired using low-cost cameras, with the aim of supporting behavioral assessment, welfare monitoring, and future clinical decision support. Methods The proposed framework combines object detection, anatomical segmentation, lightweight convolutional neural networks for posture and side-view classification, pose estimation, and rule-based geometric analysis within a structured processing pipeline. From standing side-view frames, the system generates descriptors including overall posture, head pitch, back inclination, back curvature, and tail configuration. Results In image-based evaluation, the lightweight neural networks achieved 92.6% accuracy for side-view classification and 93.1% accuracy for overall posture classification while using approximately 38 times fewer trainable parameters than VGG19-based baselines. After image quality and viewpoint filtering, descriptor accuracy reached 88% for head pitch, 95% for back inclination, 90% for back curvature, and 95.47% for tail configuration. In end-to-end video experiments, frame retention after filtering depended strongly on recording conditions, particularly side-view visibility and occlusion. Nevertheless, retained frames still enabled high-accuracy estimation of head pitch, back inclination, and tail configuration. Discussion These findings demonstrate that automated posture analysis from standard RGB video can generate structured and clinically interpretable feline body-language descriptors relevant to behavioral medicine and animal welfare assessment. Further prospective validation against established pain, stress, and welfare assessment instruments is required before clinical or diagnostic application.