Subject Level Deviation

Subject-level deviation analysis focuses on identifying and quantifying discrepancies between expected and observed behavior in various systems, ranging from robotic navigation and human motion prediction to machine learning model performance and financial trading systems. Current research employs diverse methods, including Gaussian mixture models for surface analysis, graph-based approaches for integrating planned and as-built environments, and variational autoencoders for multi-modal data analysis, to detect and characterize these deviations. This research is significant for improving the robustness and reliability of complex systems across numerous domains, enabling more accurate predictions, enhanced decision-making, and more effective anomaly detection.