Automated behavioral segmentation and markerless pose tracking of mice during spaceflight

The NASA Rodent Habitat aboard the International Space Station enabled long-duration studies of behavioral responses to spaceflight, but video-based behavioral analysis has relied on laborious manual annotation. No study has tested whether deep learning tools can automate this analysis under the demanding imaging conditions of orbital vivaria. We applied pose estimation (SLEAP) and behavioral segmentation (DeepEthogram) to archival footage from the Rodent Research-1 mission. Nine labelers annotated 3,249 pose labels across 2,063 frames, and three behaviorists labeled 411,194 frames across 66 videos. Pose tracking accuracy approximated human inter-annotator variability despite progressive lens soiling, grid occlusions, and spherical aberration. Behavioral classification across eight categories achieved accuracy of 0.86–0.90 and suggests progressive behavioral adaptations to microgravity. Kinematic reconstruction of circling estimated centripetal accelerations periodically approaching 1 g . This is the first application of deep learning-based pose estimation and behavioral segmentation to rodents in spaceflight, establishing benchmarks for future monitoring systems.