Mamba's linear-complexity architecture enables real-time cognitive load monitoring from noisy eye-tracking signals on wearable devices—a practical alternative to Transformers for temporal sensor data with frequent gaps.
MambaGaze uses a bidirectional Mamba neural network to assess cognitive load from eye-tracking data in real-time. It handles missing data from eye blinks and tracking failures by explicitly encoding uncertainty, and runs efficiently on edge devices like smartglasses for applications like driver monitoring.