Physics-Refracted Interdomain Shift Modeling for Cross-Sensor Pilot Action Recognition

Monitoring pilot manipulation behavior is central to civil aviation safety. However, calibrated multi-camera rigs cannot be installed in a cockpit, and a single deployable depth camera yields degraded skeletons. Recognition must therefore transfer across a spatiotemporal shift dominated by a change in sensing modality. This paper proposes PRISM (Physics-Refracted Interdomain Shift Modeling). PRISM combines a cascade of degradation operators with alignment layers and treats the domain shift as separable rather than opaque. Five physics-based degradation operators are cascaded at the input, reproducing the principal error mechanisms of a depth camera. Their parameters are estimated from paired recordings rather than assumed. Two feature-space layers then align marginal distributions and class-conditional prototypes. Averaged over five seeds, combining the cascade with the two alignment layers raises the target accuracy of a spatiotemporal convolutional neural network backbone from 52.85% to 98.98% without target labels. PRISM also removes the grid search over alignment strengths. A grid search over 25 strength combinations placed its optimum at the strength combination that PRISM selects. The gain was significant on all six backbones tested, and a leave-one-subject-out rotation over the four pilots gave 98.91 ± 0.41% accuracy. The framework offers a deployable route to single-sensor cockpit monitoring.